CN103885867A - Online evaluation method of performance of analog circuit - Google Patents

Online evaluation method of performance of analog circuit Download PDF

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Publication number
CN103885867A
CN103885867A CN201410123824.8A CN201410123824A CN103885867A CN 103885867 A CN103885867 A CN 103885867A CN 201410123824 A CN201410123824 A CN 201410123824A CN 103885867 A CN103885867 A CN 103885867A
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sample
fuzzy
support vector
vector regression
samples
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CN103885867B (en
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张爱华
王永超
霍星
张志强
王春杰
方辉
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Bohai University
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Bohai University
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Abstract

An online evaluation method of performance of an analog circuit includes the steps that first, types of kernel functions and parameters of fuzzy clustering support vector regression machine models are determined, a set cutting fuzzy C-mean clustering algorithm is adopted for clustering data samples, c classes are generated, the fuzzy membership degree of each class of data samples is calculated in combination with k neighboring thought, and c fuzzy clustering support vector regression machine models are generated; the generated c fuzzy clustering support vector regression machine models are used for training training samples in the data samples, the MSE of the training samples is calculated, and when the MSE is larger than the algorithm termination condition (please see the formula in the specifications), circulation is ended, and the final fuzzy clustering support vector regression machine models are determined; if not, after new samples are added in, the size relationship between the total number of the current samples and the sample capacity is judged, and incremental updating and decrement updating are performed. The method has the advantages that wrong values and interference parameters are effectively processed, evaluation accuracy is guaranteed, and online operation accuracy is improved.

Description

A kind of on-line evaluation method of mimic channel performance
Technical field
The present invention relates to a kind of on-line evaluation method of mimic channel performance, be specifically related to a kind of on-line evaluation method of the mimic channel performance based on fuzzy clustering support vector regression.
Background technology
Along with the develop rapidly of electronic technology, the ever-increasing while of the widespread use of mimic channel, complexity and closeness, its reliability of operation is required also day by day improving.And mimic channel is because of diversity, the complicacy of himself and fault thereof, makes existing Performance Evaluation Technique slower development.Existing mimic channel performance evaluation and detection method adopt the evaluation method of instrument and equipment more, and this evaluation method shortcoming is that cost is high, portable poor.
The development of the intellectual technologies such as neural network, fuzzy logic, genetic algorithm, for mimic channel performance evaluation provides the useful space, particularly support vector machine is subject to more wide research and application.Though at present all equipment evaluation methods of realizing take support vector machine as gordian technique have the advantages such as evaluation precision is excellent, portable, low cost, but factory's real current situation is not considered in its existing design, the data that gathered, because being subject to the existing wrong value of factory's actual environment and disturbing, produce wrong evaluation and test therefrom.
Summary of the invention
The technical problem to be solved in the present invention is to provide that a kind of evaluation precision is excellent, portable, low cost, fast, precisely, can realize the effectively on-line evaluation method of the mimic channel performance of processing of wrong value, interference value.
The present invention adopts following technical scheme:
An on-line evaluation method for mimic channel performance, its concrete steps are as follows:
Step 1: determine the kernel function type of fuzzy clustering support vector regression model, fuzzy clustering support vector regression model parameter has initialization clusters number c, carries out the sample size of decrement process
Figure 660579DEST_PATH_IMAGE001
, nearest neighbor point number k, algorithm end condition
Figure 814479DEST_PATH_IMAGE002
;
Step 2: adopt cut set Fuzzy C-Means Clustering Algorithm to carry out cluster to data sample, generate c class, the data sample in every class, in conjunction with k neighbour thought, is calculated to its fuzzy membership, generate c fuzzy clustering support vector regression submodel;
Step 3: use c the fuzzy clustering support vector regression submodel generating to train the training sample in data sample, calculate its root-mean-square error MSE, judge root-mean-square error MSE and algorithm end condition magnitude relationship, when root-mean-square error MSE> algorithm end condition , stop circulation, determine final fuzzy clustering support vector regression model; Otherwise, enter step 4;
Step 4: utilize in the data-optimized modeling process in rolling time window, model, along with online updating is carried out in the rolling of time window, adds after new samples, judges the magnitude relationship of current total sample number and sample size, current total sample number M≤sample size
Figure 116651DEST_PATH_IMAGE001
, carry out incremental update, return to step 2; Otherwise adopt decrement to upgrade, return to step 2.
The on-line evaluation method of above-mentioned mimic channel performance, in step 1, the kernel function of fuzzy clustering support vector regression submodel adopts gaussian kernel function, the nuclear parameter penalty factor of gaussian kernel function
Figure 243001DEST_PATH_IMAGE003
with core width
Figure 601301DEST_PATH_IMAGE004
optimizing process as follows:
1.1, first adopt the optimizing of exponential increase mode
Figure 73871DEST_PATH_IMAGE003
collection and
Figure 108692DEST_PATH_IMAGE004
collection;
1.2, by optimizing
Figure 971605DEST_PATH_IMAGE003
collection and
Figure 881399DEST_PATH_IMAGE004
grid search method optimizing parameter pair for centralized procurement carry out cross validation;
The sample set D of data sample is divided into S group
Figure 430509DEST_PATH_IMAGE006
, arbitrarily
Figure 843036DEST_PATH_IMAGE007
group as training set is
Figure 792406DEST_PATH_IMAGE008
, remain one group as checking collection
Figure 600088DEST_PATH_IMAGE009
, can repeat
Figure 55340DEST_PATH_IMAGE010
inferior, its Generalization Capability P can evaluate by through type (1),
Figure 892846DEST_PATH_IMAGE011
（1）
In formula: be
Figure 681996DEST_PATH_IMAGE013
group checking collection,
Figure 308149DEST_PATH_IMAGE014
for the input of checking collection sample,
Figure 632952DEST_PATH_IMAGE015
for the output of checking collection sample,
Figure 422660DEST_PATH_IMAGE016
for
Figure 578834DEST_PATH_IMAGE017
the parameter vector obtaining during as training sample, by regression equation group
Figure 313572DEST_PATH_IMAGE018
（2）
Obtain regression parameter
Figure 109359DEST_PATH_IMAGE019
Figure 282851DEST_PATH_IMAGE016
, wherein, for gaussian kernel function,
Figure 199172DEST_PATH_IMAGE021
for Lagrange multiplier vector,
Figure 983719DEST_PATH_IMAGE022
for bias vector; With the input of checking collection sample , obtain the output of system
Figure 498194DEST_PATH_IMAGE023
;
1.3, circulation select parameter to and carry out cross validation, calculate every pair of parameter Generalization Capability P until grid search stop, and then the parameter pair of P minimum in assurance formula (1)
Figure 824002DEST_PATH_IMAGE005
for optimum.
The on-line evaluation method of above-mentioned mimic channel performance, cut set Fuzzy C-Means Clustering in step 2, utilize the data-optimized modeling in rolling time window, model is along with online updating is carried out in the rolling of time window, realize online location to newly adding sample, and upgrade support vector regression submodel, when the mutual clustering algorithm of increase and decrease that adopts is incremental clustering algorithm:
If current sampling instant fuzzy clustering support vector regression model data sample number is M, wherein
Figure 345113DEST_PATH_IMAGE024
, N is off-line sample number;
1.1, initialization exponential factor m, generates the threshold value of new class
Figure 594829DEST_PATH_IMAGE025
, carry out the initialization clusters number d of incremental process;
1.2, calculate the cut set factor
Figure 580103DEST_PATH_IMAGE026
with the newly-increased sample of current sampling instant
Figure 575347DEST_PATH_IMAGE027
to a upper cluster centre that sampling instant is definite
Figure 583754DEST_PATH_IMAGE028
inner product norm
Figure 637161DEST_PATH_IMAGE029
and have
Figure 663892DEST_PATH_IMAGE030
;
1.3, order
Figure 144552DEST_PATH_IMAGE031
, return
Figure 374676DEST_PATH_IMAGE032
corresponding
Figure 920189DEST_PATH_IMAGE033
value, be denoted as
Figure 614475DEST_PATH_IMAGE034
; If simultaneously
Figure 203720DEST_PATH_IMAGE035
, calculate
Figure 435987DEST_PATH_IMAGE027
for
Figure 831196DEST_PATH_IMAGE033
class be subordinate to index
Figure 317672DEST_PATH_IMAGE036
, right
Figure 405714DEST_PATH_IMAGE027
be handled as follows: judgement
Figure 358233DEST_PATH_IMAGE032
with the threshold value that generates new class
Figure 229237DEST_PATH_IMAGE025
magnitude relationship;
When
Figure 898116DEST_PATH_IMAGE037
,
If
Figure 344010DEST_PATH_IMAGE038
, or
Figure 832760DEST_PATH_IMAGE039
but
Figure 773034DEST_PATH_IMAGE040
, the fuzzy membership based on clustering algorithm so ;
If
Figure 149100DEST_PATH_IMAGE039
, and , the fuzzy membership based on clustering algorithm so
Figure 56062DEST_PATH_IMAGE043
, known by maximum membership grade principle affiliated class, and by all sample substitutions in such
Figure 972382DEST_PATH_IMAGE044
（3）
Wherein,
Figure 435725DEST_PATH_IMAGE028
represent to add and produce after new samples
Figure 780118DEST_PATH_IMAGE033
the new cluster centre of class, z is the subscript of all samples in such,
Figure 278096DEST_PATH_IMAGE045
for the fuzzy membership of all samples of being obtained by cut set Fuzzy C-Means Clustering Algorithm;
When , so for new samples M sets up a new class, and using this sample as the cluster centre that increases class newly; Increase a fuzzy clustering support vector regression submodel simultaneously, and with the newly-increased model of this sample training;
1.4, compute classes centre deviation is
Figure 646987DEST_PATH_IMAGE047
,
Figure 529493DEST_PATH_IMAGE048
for the error information row that obtain after application model prediction, for all classes center median, availability deciding is carried out at Dui Lei center, when
Figure 371995DEST_PATH_IMAGE050
,
Figure 747613DEST_PATH_IMAGE051
for median deviation, such,, for disturbing, deletes such; When
Figure 433809DEST_PATH_IMAGE052
, such is wrong value, deletes such; When
Figure 827750DEST_PATH_IMAGE053
, such is normal sample, wherein
Figure 878883DEST_PATH_IMAGE054
;
1.5, along with step 1.2 is returned in the arrival of newly-increased sample, until sample number reaches Sample Maximal capacity
Figure 804113DEST_PATH_IMAGE001
.
The on-line evaluation method of above-mentioned mimic channel performance, cut set Fuzzy C-Means Clustering in step 2, model is along with online updating is carried out in the rolling of time window, utilize the data-optimized modeling in rolling time window, realize online location to newly adding sample, and upgrade support vector regression submodel, when the mutual clustering algorithm of increase and decrease that adopts is decrement clustering algorithm:
If present Fuzzy cluster support vector regression model is M because incremental learning is updated to data sample number,
Figure 713907DEST_PATH_IMAGE055
, for Sample Maximal capacity;
1.1, initialization exponential factor m, generates the threshold value of new class
Figure 263017DEST_PATH_IMAGE025
, carry out the initialization clusters number of decrement process ;
1.2, find out sample
Figure 624914DEST_PATH_IMAGE057
in corresponding class, be subordinate to minimum element, and delete this element;
1.3, will delete after all sample substitution formulas (3) in such
Figure 744180DEST_PATH_IMAGE044
, obtain such new cluster centre, wherein,
Figure 887848DEST_PATH_IMAGE028
represent to add and produce after new samples
Figure 787671DEST_PATH_IMAGE033
the new cluster centre of class, z is the subscript of all samples in such, for the fuzzy membership of all samples of being obtained by cut set Fuzzy C-Means Clustering Algorithm;
1.4, calculate the cut set factor
Figure 514504DEST_PATH_IMAGE058
with the newly-increased sample of current sampling instant
Figure 140658DEST_PATH_IMAGE027
cluster centre after upgrading to step 1.3
Figure 199880DEST_PATH_IMAGE059
inner product norm
Figure 569682DEST_PATH_IMAGE060
:
Figure 411343DEST_PATH_IMAGE061
（4）
1.5, order
Figure 208397DEST_PATH_IMAGE062
, return
Figure 754916DEST_PATH_IMAGE063
corresponding
Figure 849780DEST_PATH_IMAGE033
value, be denoted as S; If simultaneously , calculate for
Figure 862233DEST_PATH_IMAGE033
class be subordinate to index
Figure 262252DEST_PATH_IMAGE065
, right
Figure 330702DEST_PATH_IMAGE027
be handled as follows: judgement
Figure 469560DEST_PATH_IMAGE066
with the threshold value that generates new class
Figure 974359DEST_PATH_IMAGE025
magnitude relationship,
1. work as
Figure 489654DEST_PATH_IMAGE067
,
If , or but , the fuzzy membership based on clustering algorithm so
Figure 204090DEST_PATH_IMAGE071
;
If and , the fuzzy membership based on clustering algorithm so , known by maximum membership grade principle
Figure 487118DEST_PATH_IMAGE027
affiliated class, and by all sample substitutions in such
Figure 384667DEST_PATH_IMAGE074
（5）
Wherein, represent to add and produce after new samples the new cluster centre of class, z is the subscript of all samples in such,
Figure 398125DEST_PATH_IMAGE045
for the fuzzy membership of all samples of being obtained by cut set Fuzzy C-Means Clustering Algorithm;
2. work as , so for new samples M sets up a new class, and using this sample as newly-increased Lei Lei center; Increase a fuzzy clustering support vector regression submodel simultaneously, and with the newly-increased model of this sample training;
1.6, compute classes centre deviation is
Figure 972643DEST_PATH_IMAGE047
,
Figure 925162DEST_PATH_IMAGE076
for the error information row that obtain after application model prediction, for all classes center median, availability deciding is carried out at Dui Lei center, when
,
Figure 910938DEST_PATH_IMAGE051
for median deviation, such,, for disturbing, deletes such; When
Figure 665268DEST_PATH_IMAGE052
, such is wrong value, deletes such; When , such is normal sample, wherein
Figure 128927DEST_PATH_IMAGE054
;
1.7, along with step 1.2 is returned in the arrival of newly-increased sample, until evaluation procedure finishes.
The on-line evaluation method of above-mentioned mimic channel performance, cut set Fuzzy C-Means Clustering in step 2, utilizes the data-optimized modeling in rolling time window, to newly adding sample to realize online location, and upgrades support vector regression submodel;
In step 2, the fuzzy membership of Fuzzy Support Vector Regression machine is
（6）
In formula,
Figure 160916DEST_PATH_IMAGE045
for the fuzzy membership of all samples of being obtained by cut set Fuzzy C-Means Clustering Algorithm,
Figure 701619DEST_PATH_IMAGE078
for the fuzzy membership based on tight ness rating;
For training sample set , definition data distance between points
Figure 867207DEST_PATH_IMAGE080
for
Figure 268233DEST_PATH_IMAGE081
（7）
Figure 612626DEST_PATH_IMAGE078
be defined as
Figure 530510DEST_PATH_IMAGE082
（8）
In formula: k is nearest neighbor point number.
The on-line evaluation method of above-mentioned mimic channel performance, in the increment self refresh of the support vector regression model of step 4 is:
Figure 302157DEST_PATH_IMAGE083
（9）
Formula (9) represents that current sampling instant fuzzy clustering support vector regression model is existing
Figure 190479DEST_PATH_IMAGE084
individual sample, after next sampling instant data message obtains, the fuzzy clustering support vector regression model after self refresh, in formula:
，
Figure 612419DEST_PATH_IMAGE086
，
for the nuclear matrix of current sampling instant,
Figure 930585DEST_PATH_IMAGE088
,
Figure 305197DEST_PATH_IMAGE089
for vector of unit length,
Figure 512187DEST_PATH_IMAGE090
,
Figure 563320DEST_PATH_IMAGE091
,
Figure 488550DEST_PATH_IMAGE092
for model parameter after increase sample,
Figure 899809DEST_PATH_IMAGE093
for Lagrange multiplier after increment self refresh,
Figure 226885DEST_PATH_IMAGE094
for increment self refresh model amount of bias,
Figure 183340DEST_PATH_IMAGE095
for adding the output of model after new samples,
Figure 595867DEST_PATH_IMAGE003
for penalty factor;
The decrement self refresh model parameter of the support vector regression model of step 4 is
Figure 309351DEST_PATH_IMAGE096
（10）
In formula,
Figure 490934DEST_PATH_IMAGE097
for Lagrange multiplier after decrement self refresh,
Figure 883869DEST_PATH_IMAGE098
for the output of model after decrement self refresh,
Figure 518113DEST_PATH_IMAGE099
,
Figure 271174DEST_PATH_IMAGE100
for
Figure 572842DEST_PATH_IMAGE101
the element of the capable k row of k, and
Figure 136679DEST_PATH_IMAGE102
The present invention compared with prior art has following beneficial effect:
1) mimic channel performance online evaluation method proposed by the invention, compared with tradition employing exact instrument evaluation method, system cost of the present invention is lower, and real-time is more excellent, good portability, more easily popularization.
2) mimic channel performance online evaluation method proposed by the invention only adopts simple cut set Fuzzy C-cluster to combine with support vector regression, the theory of considering the self refresh of increase and decrease amount has designed mimic channel performance online evaluation method, with utilize robust, the mimic channel evaluation method of the designs such as filtering is compared, the present invention formerly evaluation method is simple in structure, data calculated amount is little, can effectively process wrong value, interference parameter etc., more can effectively promote precision and the speed of mimic channel on-line evaluation, the cost that has solved traditional instrument evaluation method is high, real-time is poor and related algorithm is complicated, calculated amount is large, overabundance of data and overflow problem of producing etc., be applicable to the on-line evaluation of the electronic product performance of mimic channel and the evaluation and test of available oscillograph.
Accompanying drawing explanation
Fig. 1 is that mimic channel performance online of the present invention is evaluated process flow diagram;
Fig. 2 is mimic channel performance online evaluation system structural drawing of the present invention;
The analog circuit test object adopting in Fig. 3 the present invention;
Fig. 4 is the actual curve figure of the mimic channel tested of the present invention;
Fig. 5 is the training sample of the mimic channel tested of the present invention and the regression curve drawing by training sample.
Embodiment
As shown in Figure 1, on-line evaluation concrete steps are as follows:
Step 1, learning training sample.
Step 2, cut set Fuzzy C-Means Clustering
Adopt cut set Fuzzy C-Means Clustering Algorithm to carry out cluster to data sample, generate c class;
Fuzzy clustering support vector regression model parameter has initialization clusters number c, carries out the sample size of decrement process
Figure 946634DEST_PATH_IMAGE001
, nearest neighbor point number k, algorithm end condition
Figure 316436DEST_PATH_IMAGE002
;
The kernel function of fuzzy clustering support vector regression model adopts gaussian kernel function, the nuclear parameter penalty factor of gaussian kernel function with core width
Figure 207348DEST_PATH_IMAGE004
optimizing process as follows:
1.1, first adopt the optimizing of exponential increase mode
Figure 3135DEST_PATH_IMAGE003
collection and collection;
1.2, by optimizing
Figure 859412DEST_PATH_IMAGE003
collection and
Figure 827368DEST_PATH_IMAGE004
grid search method optimizing parameter pair for centralized procurement carry out cross validation;
The sample set D of data sample is divided into S group
Figure 320590DEST_PATH_IMAGE006
, arbitrarily
Figure 123461DEST_PATH_IMAGE007
group as training set is
Figure 262318DEST_PATH_IMAGE008
, remain one group as checking collection
Figure 32697DEST_PATH_IMAGE009
, can repeat inferior, its Generalization Capability P can evaluate by through type (1),
Figure 205370DEST_PATH_IMAGE011
（1）
In formula:
Figure 515128DEST_PATH_IMAGE012
be group checking collection, for the input of checking collection sample,
Figure 585798DEST_PATH_IMAGE015
for the output of checking collection sample,
Figure 330769DEST_PATH_IMAGE016
for
Figure 922287DEST_PATH_IMAGE017
the parameter vector obtaining during as training sample, by regression equation group
Figure 554257DEST_PATH_IMAGE018
（2）
Obtain regression parameter
Figure 471397DEST_PATH_IMAGE019
Figure 960058DEST_PATH_IMAGE016
, wherein, for gaussian kernel function,
Figure 107322DEST_PATH_IMAGE021
for Lagrange multiplier vector, for bias vector; With the input of checking collection sample
Figure 383769DEST_PATH_IMAGE014
, obtain the output of system
Figure 18887DEST_PATH_IMAGE023
;
1.3, circulation select parameter to and carry out cross validation, calculate every pair of parameter Generalization Capability P until grid search stop, and then the parameter pair of P minimum in assurance formula (1)
Figure 376181DEST_PATH_IMAGE005
for optimum.
Step 3: in conjunction with increase and decrease amount self refresh study, calculate fuzzy membership
To generating c the data sample in class in step 2 in conjunction with k neighbour thought, calculate its fuzzy membership, generate c fuzzy clustering support vector regression submodel;
Wherein, the fuzzy membership of Fuzzy Support Vector Regression machine is
（6）
In formula, for the fuzzy membership of all samples of being obtained by cut set Fuzzy C-Means Clustering Algorithm,
Figure 798569DEST_PATH_IMAGE078
for the fuzzy membership based on tight ness rating;
For training sample set
Figure 508905DEST_PATH_IMAGE079
, definition data distance between points
Figure 938750DEST_PATH_IMAGE080
for
Figure 118058DEST_PATH_IMAGE081
（7）
Figure 393182DEST_PATH_IMAGE078
be defined as
Figure 722139DEST_PATH_IMAGE082
（8）
step 4: forecast sample is predicted, calculated its root-mean-square error;
Use c the fuzzy clustering support vector regression submodel generating to train the training sample in data sample, calculate its root-mean-square error MSE, judge root-mean-square error MSE and algorithm end condition
Figure 994988DEST_PATH_IMAGE002
magnitude relationship, when root-mean-square error MSE> algorithm end condition
Figure 723910DEST_PATH_IMAGE002
, stop circulation, determine final fuzzy clustering support vector regression model; Otherwise, enter step 5.
Step 5: utilize in the data-optimized modeling process in rolling time window, model, along with online updating is carried out in the rolling of time window, adds after new samples, judges the magnitude relationship of current total sample number and sample size, current total sample number M≤sample size
Figure 989675DEST_PATH_IMAGE001
, carry out incremental update, return to step 2; Otherwise adopt decrement to upgrade, return to step 2.
While further illustrating cut set Fuzzy C-Means Clustering, utilize the data-optimized modeling in rolling time window, model is along with online updating is carried out in the rolling of time window, realize online location to newly adding sample, and upgrade support vector regression submodel, when the mutual clustering algorithm of increase and decrease that adopts is incremental clustering algorithm:
If current sampling instant fuzzy clustering support vector regression model data sample number is M, wherein
Figure 487652DEST_PATH_IMAGE024
, N is off-line sample number;
1.1, initialization exponential factor m=2, generates the threshold value of new class
Figure 196982DEST_PATH_IMAGE025
=0.1, carry out the initialization clusters number d of incremental process;
1.2, calculate the cut set factor with the newly-increased sample of current sampling instant
Figure 718542DEST_PATH_IMAGE027
to a upper cluster centre that sampling instant is definite
Figure 71026DEST_PATH_IMAGE028
inner product norm and have
Figure 576142DEST_PATH_IMAGE030
;
1.3, order , return corresponding
Figure 520461DEST_PATH_IMAGE033
value, be denoted as
Figure 131178DEST_PATH_IMAGE034
; If simultaneously
Figure 355486DEST_PATH_IMAGE035
, calculate
Figure 354666DEST_PATH_IMAGE027
for
Figure 639017DEST_PATH_IMAGE033
class be subordinate to index
Figure 238494DEST_PATH_IMAGE036
, right
Figure 266493DEST_PATH_IMAGE027
be handled as follows: judgement
Figure 385759DEST_PATH_IMAGE032
with the threshold value that generates new class
Figure 263847DEST_PATH_IMAGE025
magnitude relationship;
When
Figure 163670DEST_PATH_IMAGE037
,
If
Figure 667464DEST_PATH_IMAGE038
, or
Figure 969132DEST_PATH_IMAGE039
but
Figure 516657DEST_PATH_IMAGE040
, the fuzzy membership based on clustering algorithm so
Figure 903776DEST_PATH_IMAGE041
;
If
Figure 211261DEST_PATH_IMAGE039
, and , the fuzzy membership based on clustering algorithm so
Figure 584397DEST_PATH_IMAGE043
, known by maximum membership grade principle
Figure 193233DEST_PATH_IMAGE027
affiliated class, and by all sample substitutions in such
Figure 304408DEST_PATH_IMAGE044
（3）
Wherein,
Figure 315089DEST_PATH_IMAGE028
represent to add and produce after new samples
Figure 469996DEST_PATH_IMAGE033
the new cluster centre of class, z is the subscript of all samples in such,
Figure 566128DEST_PATH_IMAGE045
for the fuzzy membership of all samples of being obtained by cut set Fuzzy C-Means Clustering Algorithm;
When
Figure 215415DEST_PATH_IMAGE046
, so for new samples M sets up a new class, and using this sample as the cluster centre that increases class newly; Increase a fuzzy clustering support vector regression submodel simultaneously, and with the newly-increased model of this sample training;
1.4, compute classes centre deviation is
Figure 80603DEST_PATH_IMAGE047
,
Figure 907876DEST_PATH_IMAGE048
for the error information row that obtain after application model prediction,
Figure 491304DEST_PATH_IMAGE049
for all classes center median, availability deciding is carried out at Dui Lei center, when
Figure 678703DEST_PATH_IMAGE050
,
Figure 850927DEST_PATH_IMAGE051
for median deviation, such,, for disturbing, deletes such; When
Figure 160686DEST_PATH_IMAGE052
, such is wrong value, deletes such; When
Figure 169093DEST_PATH_IMAGE053
, such is normal sample, wherein
Figure 222500DEST_PATH_IMAGE054
;
1.5, along with step 1.2 is returned in the arrival of newly-increased sample, until sample number reaches Sample Maximal capacity
Figure 747765DEST_PATH_IMAGE001
.
When the mutual clustering algorithm of increase and decrease that adopts is decrement clustering algorithm:
If present Fuzzy cluster support vector regression model is M because incremental learning is updated to data sample number,
Figure 166108DEST_PATH_IMAGE055
,
Figure 458550DEST_PATH_IMAGE001
for Sample Maximal capacity;
1.1, initialization exponential factor m=2, generates the threshold value of new class
Figure 502598DEST_PATH_IMAGE025
=0.1, carry out the initialization clusters number of decrement process
Figure 196884DEST_PATH_IMAGE056
;
1.2, find out sample
Figure 786129DEST_PATH_IMAGE057
in corresponding class, be subordinate to minimum element, and delete this element;
1.3, will delete after all sample substitution formulas (3) in such
Figure 831445DEST_PATH_IMAGE044
, obtain such new cluster centre, wherein,
Figure 915070DEST_PATH_IMAGE028
represent to add and produce after new samples
Figure 463863DEST_PATH_IMAGE033
the new cluster centre of class, z is the subscript of all samples in such,
Figure 489588DEST_PATH_IMAGE045
for the fuzzy membership of all samples of being obtained by cut set Fuzzy C-Means Clustering Algorithm;
1.4, calculate the cut set factor
Figure 756621DEST_PATH_IMAGE058
with the newly-increased sample of current sampling instant
Figure 142472DEST_PATH_IMAGE027
cluster centre after upgrading to step 1.3 inner product norm
Figure 742397DEST_PATH_IMAGE060
:
Figure 182213DEST_PATH_IMAGE061
（4）
1.5, order
Figure 919225DEST_PATH_IMAGE062
, return
Figure 380293DEST_PATH_IMAGE063
corresponding
Figure 810137DEST_PATH_IMAGE033
value, be denoted as S; If simultaneously
Figure 973134DEST_PATH_IMAGE064
, calculate
Figure 513837DEST_PATH_IMAGE027
for
Figure 94991DEST_PATH_IMAGE033
class be subordinate to index
Figure 430157DEST_PATH_IMAGE065
, right be handled as follows: judgement
Figure 926309DEST_PATH_IMAGE066
with the threshold value that generates new class
Figure 361969DEST_PATH_IMAGE025
magnitude relationship,
1. work as ,
If
Figure 271205DEST_PATH_IMAGE068
, or
Figure 153711DEST_PATH_IMAGE069
but
Figure 443878DEST_PATH_IMAGE070
, the fuzzy membership based on clustering algorithm so ;
If
Figure 509847DEST_PATH_IMAGE069
and
Figure 930464DEST_PATH_IMAGE072
, the fuzzy membership based on clustering algorithm so
Figure 340716DEST_PATH_IMAGE073
, known by maximum membership grade principle
Figure 188587DEST_PATH_IMAGE027
affiliated class, and by all sample substitutions in such
Figure 300768DEST_PATH_IMAGE074
（5）
Wherein,
Figure 462759DEST_PATH_IMAGE028
represent to add and produce after new samples
Figure 789835DEST_PATH_IMAGE033
the new cluster centre of class, z is the subscript of all samples in such,
Figure 762601DEST_PATH_IMAGE045
for the fuzzy membership of all samples of being obtained by cut set Fuzzy C-Means Clustering Algorithm;
2. work as
Figure 175128DEST_PATH_IMAGE075
, so for new samples M sets up a new class, and using this sample as newly-increased Lei Lei center; Increase a fuzzy clustering support vector regression submodel simultaneously, and with the newly-increased model of this sample training;
1.6, compute classes centre deviation is
Figure 875231DEST_PATH_IMAGE047
,
Figure 56814DEST_PATH_IMAGE076
for the error information row that obtain after application model prediction, for all classes center median, availability deciding is carried out at Dui Lei center, when
Figure 598839DEST_PATH_IMAGE050
, for median deviation, such,, for disturbing, deletes such; When
Figure 138722DEST_PATH_IMAGE052
, such is wrong value, deletes such; When
Figure 473799DEST_PATH_IMAGE053
, such is normal sample, wherein
Figure 860918DEST_PATH_IMAGE054
;
1.7, along with step 1.2 is returned in the arrival of newly-increased sample, until evaluation procedure finishes.
The increment self refresh of support vector regression model is:
Figure 902823DEST_PATH_IMAGE083
（9）
Formula (9) represents that current sampling instant fuzzy clustering support vector regression model is existing
Figure 245949DEST_PATH_IMAGE084
individual sample, after next sampling instant data message obtains, the fuzzy clustering support vector regression model after self refresh, in formula:
Figure 43003DEST_PATH_IMAGE085
，
Figure 589522DEST_PATH_IMAGE086
，
Figure 389113DEST_PATH_IMAGE087
for the nuclear matrix of current sampling instant,
Figure 586745DEST_PATH_IMAGE088
,
Figure 554701DEST_PATH_IMAGE089
for vector of unit length,
Figure 588517DEST_PATH_IMAGE090
, ,
Figure 165308DEST_PATH_IMAGE092
for model parameter after increase sample,
Figure 304166DEST_PATH_IMAGE093
for Lagrange multiplier after increment self refresh,
Figure 887594DEST_PATH_IMAGE094
for increment self refresh model amount of bias, for adding the output of model after new samples,
Figure 808069DEST_PATH_IMAGE003
for penalty factor;
The decrement self refresh model parameter of support vector regression model is
Figure 55511DEST_PATH_IMAGE096
（10）
In formula,
Figure 126235DEST_PATH_IMAGE097
for Lagrange multiplier after decrement self refresh,
Figure 366592DEST_PATH_IMAGE098
for the output of model after decrement self refresh, ,
Figure 250814DEST_PATH_IMAGE100
for
Figure 543255DEST_PATH_IMAGE101
the element of the capable k row of k, and
。
Take the mimic channel of Fig. 3 as tested object, its actual performance curve as shown in Figure 4.Mimic channel by the on-line evaluation method of mimic channel performance of the present invention for Fig. 3, as shown in Figure 2, first measurand is carried out to data acquisition, after input computer system, the Performance Appraisal System forming by the on-line evaluation method of mimic channel performance of the present invention is tested.The training sample of the mimic channel of test and pick recurrence performance curve that the training sample after default, interference parameter draws as Fig. 5.Can be found out by Fig. 4 and Fig. 5, the actual performance curve of mimic channel and the recurrence performance curve with on-line evaluation method acquisition of the present invention match, and mimic channel method of evaluating performance of the present invention is described, the performance state of energy precisetest mimic channel.
Evaluation method of the present invention can be extended in the performance online evaluation of the electronic product using oscillograph as evaluating tool.

Claims (6)

1. an on-line evaluation method for mimic channel performance, is characterized in that: its concrete steps are as follows:
Step 1: determine the kernel function type of fuzzy clustering support vector regression model, fuzzy clustering support vector regression model parameter has initialization clusters number c, carries out the sample size of decrement process
Figure 929302DEST_PATH_IMAGE001
, nearest neighbor point number k, algorithm end condition ;
Step 2: adopt cut set Fuzzy C-Means Clustering Algorithm to carry out cluster to data sample, generate c class, the data sample in every class, in conjunction with k neighbour thought, is calculated to its fuzzy membership, generate c fuzzy clustering support vector regression submodel;
Step 3: use c the fuzzy clustering support vector regression submodel generating to train the training sample in data sample, calculate its root-mean-square error MSE, judge root-mean-square error MSE and algorithm end condition magnitude relationship, when root-mean-square error MSE> algorithm end condition
Figure 445100DEST_PATH_IMAGE002
, stop circulation, determine final fuzzy clustering support vector regression model; Otherwise, enter step 4;
Step 4: utilize the data-optimized modeling in rolling time window, model, along with online updating is carried out in the rolling of time window, adds after new samples, judges the magnitude relationship of current total sample number and sample size, current total sample number M≤sample size
Figure 840309DEST_PATH_IMAGE001
, carry out incremental update, return to step 2; Otherwise adopt decrement to upgrade, return to step 2.
2. the on-line evaluation method of mimic channel performance according to claim 1, is characterized in that: in step 1, the kernel function of fuzzy clustering support vector regression submodel adopts gaussian kernel function, the nuclear parameter penalty factor of gaussian kernel function
Figure 592365DEST_PATH_IMAGE003
with core width
Figure 414827DEST_PATH_IMAGE004
optimizing process as follows:
1.1, first adopt the optimizing of exponential increase mode
Figure 681861DEST_PATH_IMAGE003
collection and
Figure 569176DEST_PATH_IMAGE004
collection;
1.2, by optimizing
Figure 238055DEST_PATH_IMAGE003
collection and grid search method optimizing parameter pair for centralized procurement
Figure 923431DEST_PATH_IMAGE005
carry out cross validation;
The sample set D of data sample is divided into S group , arbitrarily
Figure 636358DEST_PATH_IMAGE007
group as training set is
Figure 738306DEST_PATH_IMAGE008
, remain one group as checking collection , can repeat
Figure 206120DEST_PATH_IMAGE010
inferior, its Generalization Capability P can evaluate by through type (1),
（1）
In formula:
Figure 122441DEST_PATH_IMAGE012
be
Figure 585783DEST_PATH_IMAGE013
group checking collection,
Figure 117128DEST_PATH_IMAGE014
for the input of checking collection sample,
Figure 349526DEST_PATH_IMAGE015
for the output of checking collection sample,
Figure 121173DEST_PATH_IMAGE016
for
Figure 9494DEST_PATH_IMAGE017
the parameter vector obtaining during as training sample, by regression equation group
（2）
Obtain regression parameter
Figure 932899DEST_PATH_IMAGE019
, wherein,
Figure 251065DEST_PATH_IMAGE020
for gaussian kernel function, for Lagrange multiplier vector,
Figure 331202DEST_PATH_IMAGE022
for bias vector; With the input of checking collection sample , obtain the output of system
Figure 307566DEST_PATH_IMAGE023
;
1.3, circulation select parameter to and carry out cross validation, calculate every pair of parameter Generalization Capability P until grid search stop, and then the parameter pair of P minimum in assurance formula (1) for optimum.
3. the on-line evaluation method of mimic channel performance according to claim 1, it is characterized in that: cut set Fuzzy C-Means Clustering in step 2, utilize the data-optimized modeling in rolling time window, model is along with online updating is carried out in the rolling of time window, realize online location to newly adding sample, and upgrade support vector regression submodel, when the mutual clustering algorithm of increase and decrease that adopts is incremental clustering algorithm:
If current sampling instant fuzzy clustering support vector regression model data sample number is M, wherein , N is off-line sample number;
1.1, initialization exponential factor m, generates the threshold value of new class
Figure 563207DEST_PATH_IMAGE025
, carry out the initialization clusters number d of incremental process;
1.2, calculate the cut set factor
Figure 913417DEST_PATH_IMAGE026
with the newly-increased sample of current sampling instant
Figure 941416DEST_PATH_IMAGE027
to a upper cluster centre that sampling instant is definite
Figure 309949DEST_PATH_IMAGE028
inner product norm
Figure 765201DEST_PATH_IMAGE029
and have ;
1.3, order
Figure 903239DEST_PATH_IMAGE031
, return corresponding
Figure 457159DEST_PATH_IMAGE033
value, be denoted as
Figure 578699DEST_PATH_IMAGE034
; If simultaneously
Figure 135451DEST_PATH_IMAGE035
, calculate
Figure 291626DEST_PATH_IMAGE027
for
Figure 26364DEST_PATH_IMAGE033
class be subordinate to index , right
Figure 543113DEST_PATH_IMAGE027
be handled as follows: judgement
Figure 239280DEST_PATH_IMAGE032
with the threshold value that generates new class
Figure 207236DEST_PATH_IMAGE025
magnitude relationship;
When
Figure 241051DEST_PATH_IMAGE037
,
If
Figure 139606DEST_PATH_IMAGE038
, or
Figure 4794DEST_PATH_IMAGE039
but
Figure 81334DEST_PATH_IMAGE040
, the fuzzy membership based on clustering algorithm so
Figure 664762DEST_PATH_IMAGE041
;
If
Figure 868473DEST_PATH_IMAGE039
, and
Figure 588167DEST_PATH_IMAGE042
, the fuzzy membership based on clustering algorithm so
Figure 835609DEST_PATH_IMAGE043
, known by maximum membership grade principle
Figure 906333DEST_PATH_IMAGE027
affiliated class, and by all sample substitutions in such
Figure 146690DEST_PATH_IMAGE044
（3）
Wherein,
Figure 986470DEST_PATH_IMAGE028
represent to add and produce after new samples
Figure 404813DEST_PATH_IMAGE033
the new cluster centre of class, z is the subscript of all samples in such,
Figure 962833DEST_PATH_IMAGE045
for the fuzzy membership of all samples of being obtained by cut set Fuzzy C-Means Clustering Algorithm;
When
Figure 239838DEST_PATH_IMAGE046
, so for new samples M sets up a new class, and using this sample as the cluster centre that increases class newly; Increase a fuzzy clustering support vector regression submodel simultaneously, and with the newly-increased model of this sample training;
1.4, compute classes centre deviation is
Figure 934124DEST_PATH_IMAGE047
,
Figure 788948DEST_PATH_IMAGE048
for the error information row that obtain after application model prediction,
Figure 568685DEST_PATH_IMAGE049
for all classes center median, availability deciding is carried out at Dui Lei center, when
Figure 150845DEST_PATH_IMAGE050
, for median deviation, such,, for disturbing, deletes such; When
Figure 725363DEST_PATH_IMAGE052
, such is wrong value, deletes such; When
Figure 992396DEST_PATH_IMAGE053
, such is normal sample, wherein
Figure 879712DEST_PATH_IMAGE054
;
1.5, along with step 1.2 is returned in the arrival of newly-increased sample, until sample number reaches Sample Maximal capacity .
4. the on-line evaluation method of mimic channel performance according to claim 1, it is characterized in that: cut set Fuzzy C-Means Clustering in step 2, model is along with online updating is carried out in the rolling of time window, utilize the data-optimized modeling in rolling time window, realize online location to newly adding sample, and upgrade support vector regression submodel, when the mutual clustering algorithm of increase and decrease that adopts is decrement clustering algorithm:
If present Fuzzy cluster support vector regression model is M because incremental learning is updated to data sample number,
Figure 296284DEST_PATH_IMAGE055
,
Figure 33295DEST_PATH_IMAGE001
for Sample Maximal capacity;
1.1, initialization exponential factor m, generates the threshold value of new class , carry out the initialization clusters number of decrement process
Figure 986525DEST_PATH_IMAGE056
;
1.2, find out sample
Figure 165834DEST_PATH_IMAGE057
in corresponding class, be subordinate to minimum element, and delete this element;
1.3, will delete after all sample substitution formulas (3) in such
Figure 440957DEST_PATH_IMAGE044
, obtain such new cluster centre, wherein,
Figure 894548DEST_PATH_IMAGE028
represent to add and produce after new samples
Figure 229714DEST_PATH_IMAGE033
the new cluster centre of class, z is the subscript of all samples in such,
Figure 896319DEST_PATH_IMAGE045
for the fuzzy membership of all samples of being obtained by cut set Fuzzy C-Means Clustering Algorithm;
1.4, calculate the cut set factor
Figure 975133DEST_PATH_IMAGE058
with the newly-increased sample of current sampling instant
Figure 660061DEST_PATH_IMAGE027
cluster centre after upgrading to step 1.3
Figure 431708DEST_PATH_IMAGE059
inner product norm
Figure 382347DEST_PATH_IMAGE060
:
Figure 202535DEST_PATH_IMAGE061
（4）
1.5, order
Figure 555019DEST_PATH_IMAGE062
, return
Figure 185983DEST_PATH_IMAGE063
corresponding value, be denoted as S; If simultaneously
Figure 982218DEST_PATH_IMAGE064
, calculate
Figure 454787DEST_PATH_IMAGE027
for
Figure 755187DEST_PATH_IMAGE033
class be subordinate to index
Figure 680418DEST_PATH_IMAGE065
, right
Figure 842409DEST_PATH_IMAGE027
be handled as follows: judgement
Figure 903906DEST_PATH_IMAGE066
with the threshold value that generates new class
Figure 897180DEST_PATH_IMAGE025
magnitude relationship,
1. work as
Figure 309707DEST_PATH_IMAGE067
,
If
Figure 275389DEST_PATH_IMAGE068
, or
Figure 456972DEST_PATH_IMAGE069
but
Figure 833595DEST_PATH_IMAGE070
, the fuzzy membership based on clustering algorithm so
Figure 733418DEST_PATH_IMAGE071
;
If
Figure 237212DEST_PATH_IMAGE069
and
Figure 538880DEST_PATH_IMAGE072
, the fuzzy membership based on clustering algorithm so
Figure 587870DEST_PATH_IMAGE073
, known by maximum membership grade principle
Figure 974989DEST_PATH_IMAGE027
affiliated class, and by all sample substitutions in such
Figure 344790DEST_PATH_IMAGE074
（5）
Wherein,
Figure 173069DEST_PATH_IMAGE028
represent to add and produce after new samples
Figure 157074DEST_PATH_IMAGE033
the new cluster centre of class, z is the subscript of all samples in such,
Figure 703593DEST_PATH_IMAGE045
for the fuzzy membership of all samples of being obtained by cut set Fuzzy C-Means Clustering Algorithm;
2. work as , so for new samples M sets up a new class, and using this sample as newly-increased Lei Lei center; Increase a fuzzy clustering support vector regression submodel simultaneously, and with the newly-increased model of this sample training;
1.6, compute classes centre deviation is
Figure 887767DEST_PATH_IMAGE047
,
Figure 541209DEST_PATH_IMAGE076
for the error information row that obtain after application model prediction, for all classes center median, availability deciding is carried out at Dui Lei center, when
,
Figure 151816DEST_PATH_IMAGE051
for median deviation, such,, for disturbing, deletes such; When
Figure 477624DEST_PATH_IMAGE052
, such is wrong value, deletes such; When , such is normal sample, wherein
Figure 248451DEST_PATH_IMAGE054
;
1.7, along with step 1.2 is returned in the arrival of newly-increased sample, until evaluation procedure finishes.
5. the on-line evaluation method of mimic channel performance according to claim 1, it is characterized in that: cut set Fuzzy C-Means Clustering in step 2, utilize the data-optimized modeling in rolling time window, to newly adding sample to realize online location, and upgrade support vector regression submodel;
In step 2, the fuzzy membership of Fuzzy Support Vector Regression machine is
Figure 233724DEST_PATH_IMAGE077
（6）
In formula,
Figure 231898DEST_PATH_IMAGE045
for the fuzzy membership of all samples of being obtained by cut set Fuzzy C-Means Clustering Algorithm,
Figure 302623DEST_PATH_IMAGE078
for the fuzzy membership based on tight ness rating;
For training sample set
Figure 293712DEST_PATH_IMAGE079
, definition data distance between points
Figure 133492DEST_PATH_IMAGE080
for
（7）
Figure 93544DEST_PATH_IMAGE078
be defined as
（8）
In formula: k is nearest neighbor point number.
6. the on-line evaluation method of mimic channel performance according to claim 1, is characterized in that: the increment self refresh at the support vector regression model of step 4 is:
（9）
Formula (9) represents that current sampling instant fuzzy clustering support vector regression model is existing
Figure 919658DEST_PATH_IMAGE084
individual sample, after next sampling instant data message obtains, the fuzzy clustering support vector regression model after self refresh, in formula:
Figure 964975DEST_PATH_IMAGE085
， ，
Figure 33611DEST_PATH_IMAGE087
for the nuclear matrix of current sampling instant,
Figure 121652DEST_PATH_IMAGE088
, for vector of unit length,
Figure 525269DEST_PATH_IMAGE090
,
Figure 616984DEST_PATH_IMAGE091
,
Figure 875927DEST_PATH_IMAGE092
for model parameter after increase sample,
Figure 630256DEST_PATH_IMAGE093
for Lagrange multiplier after increment self refresh,
Figure 304951DEST_PATH_IMAGE094
for increment self refresh model amount of bias,
Figure 15287DEST_PATH_IMAGE095
for adding the output of model after new samples, for penalty factor;
The decrement self refresh model parameter of the support vector regression model of step 4 is
Figure 358861DEST_PATH_IMAGE096
（10）
In formula, for Lagrange multiplier after decrement self refresh, for the output of model after decrement self refresh, ,
Figure 230292DEST_PATH_IMAGE100
for
Figure 309106DEST_PATH_IMAGE101
the element of the capable k row of k, and
Figure 994034DEST_PATH_IMAGE102
。
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CN109147875A (en) * 2018-08-08 2019-01-04 合肥学院 The dissolution of contaminated water oxygen concentration prediction technique of support vector regression algorithm based on fuzzy clustering
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CN103245907A (en) * 2013-01-30 2013-08-14 中国人民解放军海军航空工程学院 Artificial circuit fault diagnosis pattern sorting algorithm

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CN102087337A (en) * 2009-12-04 2011-06-08 哈尔滨理工大学 Annealing genetic optimization method for diagnosing excitation of nonlinear analog circuit
CN103245907A (en) * 2013-01-30 2013-08-14 中国人民解放军海军航空工程学院 Artificial circuit fault diagnosis pattern sorting algorithm

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CN107218964B (en) * 2017-05-23 2020-01-24 中国人民解放军国防科学技术大学 Method for judging capacity character of test subsample
CN109147875A (en) * 2018-08-08 2019-01-04 合肥学院 The dissolution of contaminated water oxygen concentration prediction technique of support vector regression algorithm based on fuzzy clustering
CN109377110A (en) * 2018-12-13 2019-02-22 洛阳博得天策网络科技有限公司 Evaluation method and system for brand content assets
CN112485650A (en) * 2020-11-30 2021-03-12 电子科技大学 Analog circuit fault parameter range identification method based on PBI
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