CN109190675A - A kind of Fuzzy classification and device based on particle swarm optimization algorithm - Google Patents
A kind of Fuzzy classification and device based on particle swarm optimization algorithm Download PDFInfo
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Abstract
The embodiment of the present application provides a kind of Fuzzy classification and device based on particle swarm optimization algorithm, comprising: creates basic fuzzy classification model；Input is dynamically refined the particle swarm optimization algorithm of inertia weight；Utilize the rule base and membership function base of particle swarm optimization algorithm Optimization of Fuzzy disaggregated model；Utilize the rule base of test data inspection optimization and the membership function base of optimization.The present invention is optimized by rule base and membership function base of the particle swarm algorithm using optimization to fuzzy classification model, improves the convergence rate and precision of population, and especially convergence rate greatly improves, and then the accuracy of classification is greatly improved.
Description
Technical field
Intelligent algorithm technical field of the present invention, and in particular to a kind of Fuzzy classification based on particle swarm optimization algorithm and
Device.
Background technique
With the development of human sciences' technology, the requirement to classification is higher and higher, so that sometimes only by rule of thumb and professional
Knowledge is difficult to carry out exact classification, and then people gradually refer to fuzzy logic in taxology.Nineteen sixty-five U.S. mathematician
L.Zadeh first proposed the concept of fuzzy logic, indicate the birth of fuzzy mathematics.
Particle swarm algorithm (PSO) is a kind of calculating evolution technology, by doctor Eberhart and doctor Kennedy in nineteen ninety-five
It proposes.The basic thought of particle swarm optimization algorithm is optimal to find by volume cooperation and information sharing between individual in population
Solution, the advantage of particle swarm algorithm are simple, easy to accomplish, and do not have the adjusting of multi-parameter, and are widely answered at present
Use the fields such as neural metwork training, parameter optimization, system modelling.But basic particle swarm algorithm also has the defect of oneself,
When carrying out optimal solution to group's particle, very slow can be become for optimization later period convergence speed of the algorithm, for multimodal letter
Number is easier to converge to local extremum.
Summary of the invention
In view of the deficiencies of the prior art, the present invention provides a kind of Fuzzy classification and dress based on particle swarm optimization algorithm
It sets, to solve the above technical problems.
In a first aspect, the embodiment of the present application provides a kind of Fuzzy classification based on particle swarm optimization algorithm, comprising:
Create basic fuzzy classification model；
Input is dynamically refined the particle swarm optimization algorithm of inertia weight；
Utilize the rule base and membership function base of particle swarm optimization algorithm Optimization of Fuzzy disaggregated model；
Utilize the rule base of test data inspection optimization and the membership function base of optimization.
With reference to first aspect, in the first embodiment of first aspect, the basic fuzzy classification model packet of creation
It includes:
Data-oriented collection is divided into training set and test set；
Obtain the characteristic information of training set data to be sorted；
Input number, input range and the output data of fuzzy classification model are set according to the characteristic information；
Basic fuzzy classification model is generated according to input number, input range and the output data.
With reference to first aspect, in second of embodiment of first aspect, the population of the particle swarm optimization algorithm
Iterative formula includes:
xi(t+1)=wxi(t)+c1r1(pi(t)-xi(t))+c2r2(pg(t)-xi(t))；
Wherein, xiIt (t) is position of the particle i in t iteration, viIt (t) is speed of the particle i in the t times iteration；W is
Inertia weight；c1、c2It is Studying factors；r1、r2To obey the random number being uniformly distributed between [0,1]；pi(t) it is current i-th
The individual optimal value that particle search arrives, pgIt is the global optimum that up to the present all particle search arrive；
λ indicates weight factor, and effect is preferable when λ value 0.01；N indicates population, piIt (t) is particle i in current iteration
Optimal, the p of individual until numbergGlobal optimum until for current iteration number.
With reference to first aspect and second of embodiment of first aspect, in the third embodiment of first aspect,
The Studying factors of the particle swarm optimization algorithm include:
c1=2.5-rand*t/T；
c2=1.5+rand*t/T；
Wherein, t is current iteration number, and T is total the number of iterations, and rand is the random number in [0,1] range.
With reference to first aspect, in the 4th kind of embodiment of first aspect, particle swarm optimization algorithm Optimization of Fuzzy is utilized
The rule base of disaggregated model includes:
The position of random initializtion particle, and the optimal value of particle position is set as its initial position, by the optimal of population
Position is set as the optimal location of primary；
It is updated according to position and speed of the particle swarm optimization algorithm to particle；
The fitness value of particle after updating is obtained, and is carried out more according to the fitness value is optimal to individual optimal and group
Newly；
Judge the deviation between adjacent generations whether in preset specified range:
It is, then terminate iteration optimization and exports optimal value；
No, then circulation is updated according to position and speed of the particle swarm optimization algorithm to particle.
With reference to first aspect, in the 5th kind of embodiment of first aspect, optimization particle swarm algorithm Optimization of Fuzzy is utilized
The membership function base of disaggregated model includes:
The position of random initializtion particle, and the optimal value of particle position is set as its initial position, by the optimal of population
Position is set as the optimal location of primary；
It is updated according to position and speed of the particle swarm optimization algorithm to particle；
The fitness value of particle after updating is obtained, and is carried out more according to the fitness value is optimal to individual optimal and group
Newly；
Judge the deviation between adjacent generations whether in preset specified range:
It is, then terminate iteration optimization and exports optimal value；
No, then circulation is updated according to position and speed of the particle swarm optimization algorithm to particle.
Second aspect, the embodiment of the present application provide a kind of fuzzy classification device based on particle swarm optimization algorithm, comprising:
Model creating unit is configured to create basic fuzzy classification model；
Algorithm input unit is configured to the particle swarm optimization algorithm that input is dynamically refined inertia weight；
Model optimization unit is configured to the rule base using particle swarm optimization algorithm Optimization of Fuzzy disaggregated model and is subordinate to
Function library；
Model measurement unit is configured to the rule base using test data inspection optimization and the membership function base of optimization.
In conjunction with second aspect, in the first embodiment of second aspect, the model creating unit includes:
Data division module is configured to data-oriented collection being divided into training set and test set；
Feature obtains module, is configured to obtain the characteristic information of training set data to be sorted；
Parameter setting module is configured to that input number, the input of fuzzy classification model are arranged according to the characteristic information
Range and output data；
Model generation module is configured to generate basic mould according to input number, input range and the output data
Paste disaggregated model.
In conjunction with second aspect, in second of embodiment of second aspect, the model optimization unit includes:
First initialization module is configured to the position of random initializtion particle, and the optimal value of particle position is set as
The optimal location of population is set as the optimal location of primary by its initial position；
First update module is configured to be updated according to position and speed of the particle swarm optimization algorithm to particle；
First optimizing module is configured to the fitness value of particle after acquisition updates, and according to the fitness value to a
Body is optimal and group is optimal is updated；
Whether first judgment module is configured to judge the deviation between adjacent generations in preset specified range
It is interior；
First output module, is configured to, and terminates iteration optimization and exports optimal value；
First circulation module, is configured to recycle and is carried out more according to position and speed of the particle swarm optimization algorithm to particle
Newly.
In conjunction with second aspect, in the third embodiment of second aspect, the model optimization unit further include:
Second initialization module is configured to the position of random initializtion particle, and the optimal value of particle position is set as
The optimal location of population is set as the optimal location of primary by its initial position；
Second update module is configured to be updated according to position and speed of the particle swarm optimization algorithm to particle；
Second optimizing module is configured to the fitness value of particle after acquisition updates, and according to the fitness value to a
Body is optimal and group is optimal is updated；
Whether the second judgment module is configured to judge the deviation between adjacent generations in preset specified range
It is interior；
Second output module, is configured to, and terminates iteration optimization and exports optimal value；
Second circulation module, is configured to recycle and is carried out more according to position and speed of the particle swarm optimization algorithm to particle
Newly.
The third aspect provides a kind of terminal, comprising:
Processor, memory, wherein
The memory is used to store computer program,
The processor from memory for calling and running the computer program, so that terminal executes above-mentioned end
The method for holding terminal.
Fourth aspect provides a kind of computer storage medium, instruction is stored in the computer readable storage medium,
When run on a computer, so that computer executes method described in above-mentioned various aspects.
5th aspect, provides a kind of computer program product comprising instruction, when run on a computer, so that
Computer executes method described in above-mentioned various aspects.
The beneficial effects of the present invention are,
Fuzzy classification and device provided by the invention, by the particle swarm algorithm using optimization to fuzzy classification model
Rule base and membership function base optimize, improve the convergence rate and precision of population, especially convergence rate substantially mentions
Height, and then the accuracy of classification is greatly improved.
In addition, design principle of the present invention is reliable, structure is simple, has very extensive application prospect.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, for those of ordinary skill in the art
Speech, without creative efforts, is also possible to obtain other drawings based on these drawings.
Fig. 1 is the schematic flow chart of the method for the application one embodiment.
Fig. 2 is the schematic block diagram of the device of the application one embodiment.
Fig. 3 is a kind of structural schematic diagram of terminal provided in an embodiment of the present invention.
Specific embodiment
Technical solution in order to enable those skilled in the art to better understand the present invention, below in conjunction with of the invention real
The attached drawing in example is applied, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described implementation
Example is only a part of the embodiment of the present invention, instead of all the embodiments.Based on the embodiments of the present invention, this field is common
Technical staff's every other embodiment obtained without making creative work, all should belong to protection of the present invention
Range.Technical solution in order to enable those skilled in the art to better understand the present invention, below in conjunction with of the invention real
The attached drawing in example is applied, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described implementation
Example is only a part of the embodiment of the present invention, instead of all the embodiments.Based on the embodiments of the present invention, this field is common
Technical staff's every other embodiment obtained without making creative work, all should belong to protection of the present invention
Range.
Fig. 1 is the schematic flow chart of the method for the application one embodiment.Wherein, Fig. 1 executing subject can be one kind
Fuzzy classification device based on particle swarm optimization algorithm.
As shown in Figure 1, this method 100 includes:
Step 110, basic fuzzy classification model is created；
Step 120, input is dynamically refined the particle swarm optimization algorithm of inertia weight；
Step 130, the rule base and membership function base of particle swarm optimization algorithm Optimization of Fuzzy disaggregated model are utilized；
Step 140, the rule base of test data inspection optimization and the membership function base of optimization are utilized.
In order to facilitate the understanding of the present invention, below with the principle of Fuzzy classification of the present invention, in conjunction with the embodiments in it is right
The process of fuzzy classification based on particle swarm optimization algorithm, to the fuzzy classification provided by the invention based on particle swarm optimization algorithm
Method is further described.
Optionally, as the application one embodiment, the basic fuzzy classification model of creation includes:
Data-oriented collection is divided into training set and test set；
Obtain the characteristic information of training set data to be sorted；
Input number, input range and the output data of fuzzy classification model are set according to the characteristic information；
Basic fuzzy classification model is generated according to input number, input range and the output data.
Optionally, as the application one embodiment, the population iterative formula of the particle swarm optimization algorithm includes:
xi(t+1)=wxi(t)+c1r1(pi(t)-xi(t))+c2r2(pg(t)-xi(t))；
Wherein, xiIt (t) is position of the particle i in t iteration, viIt (t) is speed of the particle i in the t times iteration；W is
Inertia weight；c1、c2It is Studying factors；r1、r2To obey the random number being uniformly distributed between [0,1]；pi(t) it is current i-th
The individual optimal value that particle search arrives, pgIt is the global optimum that up to the present all particle search arrive；
λ indicates weight factor, and effect is preferable when λ value 0.01；N indicates population, piIt (t) is particle i in current iteration
Optimal, the p of individual until numbergGlobal optimum until for current iteration number.
Optionally, as the application one embodiment, the Studying factors of the particle swarm optimization algorithm include:
c1=2.5-rand*t/T；
c2=1.5+rand*t/T；
Wherein, t is current iteration number, and T is total the number of iterations, and rand is the random number in [0,1] range.
Optionally, as the application one embodiment, the rule of particle swarm optimization algorithm Optimization of Fuzzy disaggregated model is utilized
Library includes:
The position of random initializtion particle, and the optimal value of particle position is set as its initial position, by the optimal of population
Position is set as the optimal location of primary；
It is updated according to position and speed of the particle swarm optimization algorithm to particle；
The fitness value of particle after updating is obtained, and is carried out more according to the fitness value is optimal to individual optimal and group
Newly；
Judge the deviation between adjacent generations whether in preset specified range:
It is, then terminate iteration optimization and exports optimal value；
No, then circulation is updated according to position and speed of the particle swarm optimization algorithm to particle.
Optionally, as the application one embodiment, being subordinate to for particle swarm optimization algorithm Optimization of Fuzzy disaggregated model is utilized
Function library includes:
The position of random initializtion particle, and the optimal value of particle position is set as its initial position, by the optimal of population
Position is set as the optimal location of primary；
It is updated according to position and speed of the particle swarm optimization algorithm to particle；
The fitness value of particle after updating is obtained, and is carried out more according to the fitness value is optimal to individual optimal and group
Newly；
Judge the deviation between adjacent generations whether in preset specified range:
It is, then terminate iteration optimization and exports optimal value；
No, then circulation is updated according to position and speed of the particle swarm optimization algorithm to particle.
Specifically, the SSD steady state detecting method for use based on FIO includes:
S1, the basic fuzzy classification model of creation.
A given data set (the present embodiment chooses Iris and wine data set), obtains the species number of the data, and
Data set is divided into training set and test set two parts, wherein the random 70% data conduct of unduplicated selection from data set
Training set, training set will guarantee that every a kind of data will exist, training set be chosen again if being unsatisfactory for, until meeting condition
Until, it just can guarantee that fuzzy controller possesses all possible output in this way, corresponding remaining 30% is used as test set；
Concentrated from training data and obtain some features of data to be sorted, the number including attribute, the range of attribute and point
The number of class, wherein the number of attribute is the number of Fuzzy Classifier input, and the range of attribute is the range accordingly inputted, number
According to classification results be fuzzy classification output；
According to the characteristic obtained, a basic T-S fuzzy classification model is generated.
S2, input are dynamically refined the particle swarm optimization algorithm of inertia weight.
Improved particle swarm optimization algorithm are as follows:
xi(t+1)=wxi(t)+c1r1(pi(t)-xi(t))+c2r2(pg(t)-xi(t))；
Wherein, xiIt (t) is position of the particle i in t iteration, viIt (t) is speed of the particle i in the t times iteration；W is
Inertia weight；c1、c2It is Studying factors；r1、r2To obey the random number being uniformly distributed between [0,1]；pi(t) it is current i-th
The individual optimal value that particle search arrives, pgIt is the global optimum that up to the present all particle search arrive；
λ indicates weight factor, and effect is preferable when λ value 0.01；N indicates population, piIt (t) is particle i in current iteration
Optimal, the p of individual until numbergGlobal optimum until for current iteration number.
Wherein Studying factors are as follows:
c1=2.5-rand*t/T；
c2=1.5+rand*t/T；
Wherein, t is current iteration number, and T is total the number of iterations, and rand is the random number in [0,1] range.
S4, the rule base and membership function base of particle swarm optimization algorithm Optimization of Fuzzy disaggregated model are utilized.
Four inputs of four attributes of Iris data intensive data as Fuzzy Classifier, the degree of membership letter of each input
Number takes 2 (or multiple), so one shares 16 fuzzy control rules.With improvement particle swarm algorithm Fuzzy Classifier
Fuzzy rule optimizes, and chooses error in classification rate as fitness function, and error rate is smaller to illustrate that classifier is better, particle
Number takes 10, and maximum number of iterations takes 50.
The specific steps in principle of optimality library are as follows:
Step1: random initializtion particle.Wherein particle dimension is 16, represents the conclusion of 16 rules in rule base.With
Machine initializes the position of particle, the optimal value pbest of each positioniIt is set as its initial position, the optimal location gbest of population is set
For the optimal location of primary；
Step2: it is updated as needed according to position and speed of the more new formula of particle swarm algorithm to particle；
Step3: the fitness value of particle after updating is calculated, and according to the fitness to the optimal progress of individual optimal and group
It updates；
Step4: judge whether to terminate optimizing, the stop condition of iteration be judged, if meeting iteration stopping item
Part then stops and exports optimal value, if conditions are not met, return step 2 continues iteration, until the operation for meeting particle stops
Only condition.
Optimize the specific steps of membership function base are as follows:
Step1: random initializtion particle.Particle dimension is 24 at this time, represents the inflection point of four input subordinating degree functions,
Fitness function also goes classification error rate.The position of random initializtion particle, the optimal value pbest of each positioniAt the beginning of being set as it
Beginning position, the optimal location gbest of population are set as the optimal location of primary；
Step2: it is updated as needed according to position and speed of the more new formula of particle swarm algorithm to particle；
Step3: the fitness value of particle after updating is calculated, and according to the fitness to the optimal progress of individual optimal and group
It updates；
Step4: judge whether to terminate optimizing, the stop condition of iteration be judged, if meeting iteration stopping item
Part then stops and exports optimal value, if conditions are not met, return step 2 continues iteration, until the operation for meeting particle stops
Only condition.
S4, the rule base of test data inspection optimization and the membership function base of optimization are utilized.
It brings the good Fuzzy Classifier of optimization into test data to test, similarly, wine data set be carried out same
The processing of sample.
If Fig. 2 shows, which includes:
Model creating unit 210, the model creating unit is for creating basic fuzzy classification model；
Algorithm input unit 220, the algorithm input unit is for inputting the particle group optimizing for being dynamically refined inertia weight
Algorithm；
Model optimization unit 230, the model optimization unit are used for mould of classifying using particle swarm optimization algorithm Optimization of Fuzzy
The rule base and membership function base of type；
Model measurement unit 240, the model measurement unit are used to utilize the rule base of test data inspection optimization and excellent
The membership function base of change.
Optionally, as the application one embodiment, the model creating unit includes:
Data division module is configured to data-oriented collection being divided into training set and test set；
Feature obtains module, is configured to obtain the characteristic information of training set data to be sorted；
Parameter setting module is configured to that input number, the input of fuzzy classification model are arranged according to the characteristic information
Range and output data；
Model generation module is configured to generate basic mould according to input number, input range and the output data
Paste disaggregated model.
Optionally, as the application one embodiment, the model optimization unit includes:
First initialization module is configured to the position of random initializtion particle, and the optimal value of particle position is set as
The optimal location of population is set as the optimal location of primary by its initial position；
First update module is configured to be updated according to position and speed of the particle swarm optimization algorithm to particle；
First optimizing module is configured to the fitness value of particle after acquisition updates, and according to the fitness value to a
Body is optimal and group is optimal is updated；
Whether first judgment module is configured to judge the deviation between adjacent generations in preset specified range
It is interior；
First output module, is configured to, and terminates iteration optimization and exports optimal value；
First circulation module, is configured to recycle and is carried out more according to position and speed of the particle swarm optimization algorithm to particle
Newly.
Optionally, as the application one embodiment, the model optimization unit further include:
Second initialization module is configured to the position of random initializtion particle, and the optimal value of particle position is set as
The optimal location of population is set as the optimal location of primary by its initial position；
Second update module is configured to be updated according to position and speed of the particle swarm optimization algorithm to particle；
Second optimizing module is configured to the fitness value of particle after acquisition updates, and according to the fitness value to a
Body is optimal and group is optimal is updated；
Whether the second judgment module is configured to judge the deviation between adjacent generations in preset specified range
It is interior；
Second output module, is configured to, and terminates iteration optimization and exports optimal value；
Second circulation module, is configured to recycle and is carried out more according to position and speed of the particle swarm optimization algorithm to particle
Newly.
Fig. 3 is a kind of structural schematic diagram of terminal installation 300 provided in an embodiment of the present invention, which can be with
For executing the method provided by the embodiments of the present application for updating heat dissipation policing parameter.
Wherein, which may include: processor 310, memory 320 and communication unit 330.These components
It is communicated by one or more bus, it will be understood by those skilled in the art that the structure of server shown in figure is not
The restriction to the application is constituted, it is also possible to hub-and-spoke configuration either busbar network, can also include more than illustrating
Or less component, perhaps combine certain components or different component layouts.
Wherein, which can be used for executing instruction for storage processor 310, and memory 320 can be by any class
The volatibility or non-volatile memories terminal or their combination of type are realized, such as static random access memory (SRAM), electricity
Erasable Programmable Read Only Memory EPROM (EEPROM), Erasable Programmable Read Only Memory EPROM (EPROM), programmable read only memory
(PROM), read-only memory (ROM), magnetic memory, flash memory, disk or CD.When executing instruction in memory 320
When being executed by processor 310, so that terminal 300 some or all of is able to carry out in following above method embodiment step.
Processor 310 is the control centre for storing terminal, utilizes each of various interfaces and the entire electric terminal of connection
A part by running or execute the software program and/or module that are stored in memory 320, and calls and is stored in storage
Data in device, to execute the various functions and/or processing data of electric terminal.The processor can be by integrated circuit
(Integrated Circuit, abbreviation IC) composition, such as the IC that can be encapsulated by single are formed, can also be by more of connection
The encapsulation IC of identical function or different function and form.For example, processor 310 can only include central processing unit
(Central Processing Unit, abbreviation CPU).In the application embodiment, CPU can be single operation core, can also
To include multioperation core.
Communication unit 330, for establishing communication channel, so that the storage terminal be allow to be led to other terminals
Letter.It receives the user data of other terminals transmission or sends user data to other terminals.
The application also provides a kind of computer storage medium, wherein the computer storage medium can be stored with program, the journey
Sequence may include step some or all of in each embodiment provided by the present application when executing.The storage medium can for magnetic disk,
CD, read-only memory (English: read-only memory, referred to as: ROM) or random access memory (English:
Random access memory, referred to as: RAM) etc..
Therefore, the application passes through the rule base and membership function base using the particle swarm algorithm optimized to fuzzy classification model
It optimizes, improves the convergence rate and precision of population, especially convergence rate greatly improves, and then is greatly improved point
The accuracy of class, the attainable technical effect of the present embodiment institute may refer to described above, and details are not described herein again.
It is required that those skilled in the art can be understood that the technology in the embodiment of the present application can add by software
The mode of general hardware platform realize.Based on this understanding, the technical solution in the embodiment of the present application substantially or
Say that the part that contributes to existing technology can be embodied in the form of software products, which is stored in
Such as USB flash disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory in one storage medium
The various media that can store program code such as (RAM, Random Access Memory), magnetic or disk, including it is several
Instruction is used so that a terminal (can be personal computer, server or second terminal, the network terminal etc.) is held
Row all or part of the steps of the method according to each embodiment of the present invention.
Same and similar part may refer to each other between each embodiment in this specification.Implement especially for terminal
For example, since it is substantially similar to the method embodiment, so being described relatively simple, related place is referring in embodiment of the method
Explanation.
In several embodiments provided herein, it should be understood that disclosed systems, devices and methods, it can be with
It realizes by another way.For example, the apparatus embodiments described above are merely exemplary, for example, the unit
It divides, only a kind of logical function partition, there may be another division manner in actual implementation, such as multiple units or components
It can be combined or can be integrated into another system, or some features can be ignored or not executed.Another point, it is shown or
The mutual coupling, direct-coupling or communication connection discussed can be through some interfaces, the indirect coupling of device or unit
It closes or communicates to connect, can be electrical property, mechanical or other forms.
The unit as illustrated by the separation member may or may not be physically separated, aobvious as unit
The component shown may or may not be physical unit, it can and it is in one place, or may be distributed over multiple
In network unit.It can select some or all of unit therein according to the actual needs to realize the mesh of this embodiment scheme
's.
It, can also be in addition, the functional units in various embodiments of the present invention may be integrated into one processing unit
It is that each unit physically exists alone, can also be integrated in one unit with two or more units.
Although by reference to attached drawing and combining the mode of preferred embodiment to the present invention have been described in detail, the present invention
It is not limited to this.Without departing from the spirit and substance of the premise in the present invention, those of ordinary skill in the art can be to the present invention
Embodiment carry out various equivalent modifications or substitutions, and these modifications or substitutions all should in covering scope of the invention/appoint
What those familiar with the art in the technical scope disclosed by the present invention, can easily think of the change or the replacement, answer
It is included within the scope of the present invention.Therefore, protection scope of the present invention is answered described is with scope of protection of the claims
It is quasi-.
Claims (10)
1. a kind of Fuzzy classification based on particle swarm optimization algorithm, which is characterized in that the described method includes:
Create basic fuzzy classification model；
Input is dynamically refined the particle swarm optimization algorithm of inertia weight；
Utilize the rule base and membership function base of particle swarm optimization algorithm Optimization of Fuzzy disaggregated model；
Utilize the rule base of test data inspection optimization and the membership function base of optimization.
2. the method according to claim 1, wherein the basic fuzzy classification model of creation includes:
Data-oriented collection is divided into training set and test set；
Obtain the characteristic information of training set data to be sorted；
Input number, input range and the output data of fuzzy classification model are set according to the characteristic information；
Basic fuzzy classification model is generated according to input number, input range and the output data.
3. the method according to claim 1, wherein the population iterative formula packet of the particle swarm optimization algorithm
It includes:
xi(t+1)=wxi(t)+c1r1(pi(t)-xi(t))+c2r2(pg(t)-xi(t))；
Wherein, xiIt (t) is position of the particle i in t iteration, viIt (t) is speed of the particle i in the t times iteration；W is inertia
Weight；c1、c2It is Studying factors；r1、r2To obey the random number being uniformly distributed between [0,1]；piIt (t) is current i-th of particle
The individual optimal value searched, pgIt is the global optimum that up to the present all particle search arrive；
λ indicates weight factor, and effect is preferable when λ value 0.01；N indicates population, piIt (t) is that particle i is in current iteration number
It is only individual optimal, pgGlobal optimum until for current iteration number.
4. according to the method described in claim 3, it is characterized in that, the Studying factors of the particle swarm optimization algorithm include:
c1=2.5-rand*t/T；
c2=1.5+rand*t/T；
Wherein, t is current iteration number, and T is total the number of iterations, and rand is the random number in [0,1] range.
5. the method according to claim 1, wherein utilizing particle swarm optimization algorithm Optimization of Fuzzy disaggregated model
Rule base includes:
The position of random initializtion particle, and the optimal value of particle position is set as its initial position, by the optimal location of population
It is set as the optimal location of primary；
It is updated according to position and speed of the particle swarm optimization algorithm to particle；
The fitness value of particle after updating is obtained, and optimal and group is optimal is updated to individual according to the fitness value；
Judge the deviation between adjacent generations whether in preset specified range:
It is, then terminate iteration optimization and exports optimal value；
No, then circulation is updated the position and speed of particle according to optimization particle swarm algorithm.
6. the method according to claim 1, wherein utilizing optimization particle swarm algorithm Optimization of Fuzzy disaggregated model
Membership function base includes:
The position of random initializtion particle, and the optimal value of particle position is set as its initial position, by the optimal location of population
It is set as the optimal location of primary；
It is updated according to position and speed of the particle swarm optimization algorithm to particle；
The fitness value of particle after updating is obtained, and optimal and group is optimal is updated to individual according to the fitness value；
Judge the deviation between adjacent generations whether in preset specified range:
It is, then terminate iteration optimization and exports optimal value；
No, then circulation is updated according to position and speed of the particle swarm optimization algorithm to particle.
7. a kind of fuzzy classification device based on particle swarm optimization algorithm, which is characterized in that described device includes:
Model creating unit is configured to create basic fuzzy classification model；
Algorithm input unit is configured to the particle swarm optimization algorithm that input is dynamically refined inertia weight；
Model optimization unit is configured to rule base and membership function using particle swarm optimization algorithm Optimization of Fuzzy disaggregated model
Library；
Model measurement unit is configured to the rule base using test data inspection optimization and the membership function base of optimization.
8. device according to claim 7, which is characterized in that the model creating unit includes:
Data division module is configured to data-oriented collection being divided into training set and test set；
Feature obtains module, is configured to obtain the characteristic information of training set data to be sorted；
Parameter setting module is configured to that input number, the input range of fuzzy classification model are arranged according to the characteristic information
And output data；
Model generation module is configured to generate basic fuzzy point according to the input number, input range and output data
Class model.
9. device according to claim 7, which is characterized in that the model optimization unit includes:
First initialization module is configured to the position of random initializtion particle, and at the beginning of the optimal value of particle position is set as it
The optimal location of population is set as the optimal location of primary by beginning position；
First update module is configured to be updated according to position and speed of the particle swarm optimization algorithm to particle；
First optimizing module, be configured to obtain update after particle fitness value, and according to the fitness value to individual most
It is excellent and group is optimal is updated；
Whether first judgment module is configured to judge the deviation between adjacent generations in preset specified range；
First output module, is configured to, and terminates iteration optimization and exports optimal value；
First circulation module, is configured to recycle and is updated according to position and speed of the particle swarm optimization algorithm to particle.
10. device according to claim 7, which is characterized in that the model optimization unit further include:
Second initialization module is configured to the position of random initializtion particle, and at the beginning of the optimal value of particle position is set as it
The optimal location of population is set as the optimal location of primary by beginning position；
Second update module is configured to be updated according to position and speed of the particle swarm optimization algorithm to particle；
Second optimizing module, be configured to obtain update after particle fitness value, and according to the fitness value to individual most
It is excellent and group is optimal is updated；
Whether the second judgment module is configured to judge the deviation between adjacent generations in preset specified range；
Second output module, is configured to, and terminates iteration optimization and exports optimal value；
Second circulation module, is configured to recycle and is updated according to position and speed of the particle swarm optimization algorithm to particle.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111008126A (en) * | 2019-11-13 | 2020-04-14 | 浙江大学 | Fuzzy test variation scheduling method and system based on particle swarm optimization |
| CN114912697A (en) * | 2022-05-26 | 2022-08-16 | 南方电网电力科技股份有限公司 | Boiler slagging degree prediction method based on PSO algorithm and related device |
| CN115357777A (en) * | 2022-08-26 | 2022-11-18 | 福建师范大学 | Fuzzy theory-based user label weight evaluation method |
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2018
- 2018-08-06 CN CN201810884160.5A patent/CN109190675A/en active Pending
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111008126A (en) * | 2019-11-13 | 2020-04-14 | 浙江大学 | Fuzzy test variation scheduling method and system based on particle swarm optimization |
| CN114912697A (en) * | 2022-05-26 | 2022-08-16 | 南方电网电力科技股份有限公司 | Boiler slagging degree prediction method based on PSO algorithm and related device |
| CN114912697B (en) * | 2022-05-26 | 2024-06-21 | 南方电网电力科技股份有限公司 | PSO algorithm-based boiler slagging degree prediction method and related device |
| CN115357777A (en) * | 2022-08-26 | 2022-11-18 | 福建师范大学 | Fuzzy theory-based user label weight evaluation method |
| CN115357777B (en) * | 2022-08-26 | 2023-09-01 | 福建师范大学 | Fuzzy theory-based user tag weight evaluation method |
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