CN109886249A - A kind of spring spring bag body based on ELMAN neural network tests evaluation method and system - Google Patents
A kind of spring spring bag body based on ELMAN neural network tests evaluation method and system Download PDFInfo
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Abstract
The present invention provides a kind of, and the spring spring bag body based on ELMAN neural network tests evaluation method and system, by developing Mobile phone App, obtains user and be transferred to cloud using different types of spring spring bag process video (can be taken on site or read video file by mobile phone A pp)；The video is resolved into continuous serial-gram beyond the clouds；Using face recognition technology, identify the corresponding human face expression type of the serial-gram, the code vector that changes over time of expression is obtained, in cloud platform, the complex nonlinear relational model that is scored by ELMAN neural network user experience data with corresponding user experience process；The typing for carrying out video can automatically obtain the user experience evaluation result of the user experience process, and the foundation of self-closing disease spring spring bag product up-gradation optimization is carried out as enterprise.
Description
Technical field
The present invention relates to big data fields, and in particular to a kind of spring spring bag body based on ELMAN neural network tests evaluation side
Method and system.
Background technique
Nowadays, the period that positive value mental health crisis is got worse, especially young man.According to " high religion in 2015
Educate record event report " a report in point out, suicide is the second largest killer of university student's death, is only second to traffic accident；From 1999
Since year, the whole homicide rate in the U.S. has risen violently about 25%.For those with self-closing disease, SPD (feel disorder), depression,
The excessive user of hypoevolutism crowd or only pressure.Some researches show that claim to pass through balance as spring spring bag
With resistance sense by, the spatial perception ability and the whole body coordination ability of disorder and hypoevolutism children are felt in training during exercise,
It can effectively mitigate the generally existing intense strain of patient, allow user to be easier association in the state of loosening and interacted with other people
Exchange.It is embedded in Emotion identification system, the emotional change during Patient Experience is acquired, calculate, is analyzed, in most cases
Under still can be used as enterprise carry out spring spring bag product up-gradation optimization foundation.
In spring spring bag product optimization development, engineers and technicians are unable to quick obtaining modified and jump the prior art
The user experience data of bag, and then Fast Evaluation cannot be made to product optimization result.
Summary of the invention
In order to solve in present R & D of complex, research staff is unable to quick obtaining modified spring spring bag user experience number
According to the problem of, the application provides a kind of spring spring bag body based on ELMAN neural network and tests evaluation method, includes the following steps
S1: acquisition user uses the first process video of spring spring bag, obtains the first process according to first process video
Serial-gram carries out recognition of face to the first process families photo and obtains user's human face expression vector, according to the user
Human face expression vector obtains input matrix,；
S2: acquisition user investigation data obtain matrix of consequence Y, the ELMAN mould of building according to the user investigation data
Type is trained ELMAN model using the input matrix and the matrix of consequence.
S3: acquisition user uses the second process video of spring spring bag, and the ELMAN model completed using training is to the user
It is analyzed using the second process video of spring spring bag and obtains user experience data.
Further, the step S1 includes,
S11: using abscissa as the time, ordinate is that expression type code generation user's human face expression vector changes over time
Two-dimentional expression spectrum, wherein " indignation " corresponding expression vector be [0,0,0,0,0,0,1]T, " detest " corresponding expression vector
For [0,0,0,0,0,2,0]T, " fear " corresponding expression vector be [0,0,0,0,3,0,0]T, " happiness " corresponding expression vector
For [0,0,0,4,0,0,0]T, " sad " corresponding expression vector be [0,0,5,0,0,0,0]T, " surprised " corresponding expression vector
For [0,6,0,0,0,0,0]T, " loss of emotion " corresponding expression vector be [7,0,0,0,0,0,0]T, compose to obtain matrix using expression
A=[e1,e2,e3,…,en]7×n；
S12: matrix A progress transposed transform is obtained into AT=[e1,e2,e3,…,en]n×7；
S13: structural matrix M=AAT；
S14: the characteristic value of calculating matrix M, eigenvalue matrix λ=[λ of generator matrix M1,λ2,λ3,…,λ7]1×7；
S15: it generates input matrix X=[λ, N, B]1×9, wherein N is the age, and B is gender.
Further, in step S2, X is setk=[xk1,xk2,…,xkM] (k=1,2 ..., S) be input vector, S be instruction
Practice number of samples, WMI(g) be the g times iteration when input layer M and hidden layer I between weighted vector, WJP(g) be the g times iteration when
Weighted vector between hidden layer J and output layer P, WJC(g) be the g time iteration when hidden layer J and undertaking layer C between weighted vector,
Yk(g)=[yk1(g),yk2(g),…,ykP(g)] reality output of network, d when (k=1,2 ..., S) is the g times iterationk=
[dk1,dk2,…,dkP] (k=1,2 ..., S) it is desired output.
Further, the step S2 includes
S21: initialization is assigned to W if the number of iterations g initial value is 0 respectivelyMI(0)、WJP(0)WJC(0) (0,1) section
Random value；
S22: stochastic inputs sample Xk；
S23: to input sample Xk, the input signal and output signal of every layer of neuron of forward calculation neural network；
S24: according to desired output dkWith reality output Yk(g), error E (g) is calculated；
Whether S25: error in judgement E (g) meet the requirements, and is such as unsatisfactory for, then enters step S26, such as meets, then enters step
S29；
S26: judging whether the number of iterations g+1 is greater than maximum number of iterations, such as larger than, then enters step S29, otherwise, into
Enter step S27；
S27: to input sample XkThe partial gradient δ of every layer of neuron of retrospectively calculate；
S28: modified weight amount Δ W is calculated, and corrects weight；G=g+1 is enabled, go to step S23；
S29: judging whether to complete all training samples, if it is, completing modeling, otherwise, continues to go to step
S22。
Further, the step S3 further includes,
User experience data is sent to administrator's mobile terminal and is shown.
In order to guarantee the implementation of the above method, the present invention also provides a kind of, and the spring spring bag body based on ELMAN neural network is tested
Evaluation system, which is characterized in that comprise the following modules
Acquisition module uses the first process video of spring spring bag for acquiring user, obtains according to first process video
To the first process families photo, recognition of face is carried out to the first process families photo and obtains user's human face expression vector, according to
Input matrix is obtained according to user's human face expression vector,；
Training module obtains result square according to the first user investigation data for acquiring the first user investigation data
Battle array Y, the ELMAN model of building are trained ELMAN model using the input matrix and the matrix of consequence.
As a result output module uses the second process video of spring spring bag for acquiring user, the ELMAN completed using training
Model is analyzed the user using the second process video of spring spring bag and obtains storage user experience data.
Further, the acquisition module obtains input matrix using following steps,
S11: using abscissa as the time, ordinate is that expression type code generation user's human face expression vector changes over time
Two-dimentional expression spectrum, wherein " indignation " corresponding expression vector be [0,0,0,0,0,0,1]T, " detest " corresponding expression vector
For [0,0,0,0,0,2,0]T, " fear " corresponding expression vector be [0,0,0,0,3,0,0]T, " happiness " corresponding expression vector
For [0,0,0,4,0,0,0]T, " sad " corresponding expression vector be [0,0,5,0,0,0,0]T, " surprised " corresponding expression vector
For [0,6,0,0,0,0,0]T, " loss of emotion " corresponding expression vector be [7,0,0,0,0,0,0]T, compose to obtain matrix using expression
A=[e1,e2,e3,…,en]7×n；
S12: matrix A progress transposed transform is obtained into AT=[e1,e2,e3,…,en]n×7；
S13: structural matrix M=AAT；
S14: the characteristic value of calculating matrix M, eigenvalue matrix λ=[λ of generator matrix M1,λ2,λ3,…,λ7]1×7；
S15: it generates input matrix X=[λ, N, B]1×9, wherein N is the age, and B is gender.
Further, X is arranged in the training modulek=[xk1,xk2,…,xkM] (k=1,2 ..., S) be input vector, S
For training sample number, WMI(g) be the g times iteration when input layer M and hidden layer I between weighted vector, WJP(g) repeatedly for the g times
For when hidden layer J and output layer P between weighted vector, WJC(g) be the g time iteration when hidden layer J and accept layer C between weight arrow
Amount, Yk(g)=[yk1(g),yk2(g),…,ykP(g)] reality output of network, d when (k=1,2 ..., S) is the g times iterationk=
[dk1,dk2,…,dkP] (k=1,2 ..., S) it is desired output.
Further, the training module is trained ELMAN model using following steps:
S21: initialization is assigned to W if the number of iterations g initial value is 0 respectivelyMI(0)、WJP(0)WJC(0) (0,1) section
Random value；
S22: stochastic inputs sample Xk；
S23: to input sample Xk, the input signal and output signal of every layer of neuron of forward calculation neural network；
S24: according to desired output dkWith reality output Yk(g), error E (g) is calculated；
Whether S25: error in judgement E (g) meet the requirements, and is such as unsatisfactory for, then enters step S26, such as meets, then enters step
S29；
S26: judging whether the number of iterations g+1 is greater than maximum number of iterations, such as larger than, then enters step S29, otherwise, into
Enter step S27；
S27: to input sample XkThe partial gradient δ of every layer of neuron of retrospectively calculate；
S28: modified weight amount Δ W is calculated, and corrects weight；G=g+1 is enabled, go to step S23；
S29: judging whether to complete all training samples, if it is, completing modeling, otherwise, continues to go to step
S22。
Further, the result output module is also used to, and user experience data is sent to administrator's mobile terminal simultaneously
It is shown.
The invention has the advantages that
1 follows the anatomy such as nerves and muscles, has common trait；Expression Recognition is under a kind of unconscious, free state
Data capture method, ensure that the reliability and objectivity of data.
2, which are easily integrated into data analysis system, is analyzed and is visualized.
3 allow the data collection of other software real time access facial expression analysis system.
4 can analyze the facial expression of all races, the facial expression including children.
5 present invention divide user in the video using spring spring bag process by the neural network model that training is completed
Analysis quickly obtains user experience data, can be convenient research staff and quickly assesses modified spring spring bag, improves spring and jumps
The efficiency of research and development of bag.
Detailed description of the invention
Fig. 1 is that a kind of spring spring bag body based on ELMAN neural network of the present invention tests evaluation method flow chart.
Fig. 2 is that a kind of spring spring bag body based on ELMAN neural network of the present invention tests evaluation system structural schematic diagram.
Fig. 3 is that one embodiment of the invention two dimension expression composes schematic diagram.
Fig. 4 is one embodiment of the invention ELMAN neural network schematic diagram.
Specific embodiment
In the following description, for purposes of illustration, it in order to provide the comprehensive understanding to one or more embodiments, explains
Many details are stated.It may be evident, however, that these embodiments can also be realized without these specific details.
For in R & D of complex, research staff is unable to asking for quick obtaining modified spring spring bag user experience data
Topic, a kind of spring spring bag body based on ELMAN neural network of the present invention test evaluation method and system
The present invention is trained ELMAN model by acquisition user video and user investigation data, is completed by training
ELMAN model to user use modified spring spring bag video identification, the user experience data of quick obtaining user.
Wherein, it should be noted that ELMAN network can be regarded as one, and there is local memory unit and LOCAL FEEDBACK to connect
The recurrent neural network connect.
Hereinafter, specific embodiments of the present invention will be described in detail with reference to the accompanying drawings.
Evaluation method is tested in order to illustrate the spring spring bag body provided by the invention based on ELMAN neural network, Fig. 1 shows this
It invents a kind of spring spring bag body based on ELMAN neural network and tests evaluation method flow chart.
As shown in Figure 1, a kind of spring spring bag body based on ELMAN neural network provided by the invention test evaluation method include with
Lower step,
S1: acquisition user uses the first process video of spring spring bag, obtains the first process according to first process video
Serial-gram carries out recognition of face to the first process families photo and obtains user's human face expression vector, according to the user
Human face expression vector obtains input matrix；
S2: acquisition user investigation data obtain matrix of consequence Y, the ELMAN mould of building according to the user investigation data
Type is trained ELMAN model using the input matrix and the matrix of consequence；
S3: acquisition user uses the second process video of spring spring bag, and the ELMAN model completed using training is to the user
It is analyzed using the second process video of spring spring bag and obtains user experience data.
First process video, the first process families photo are the training data for training neural network model, and second
Process video is data to be tested, and trained neural network carries out analysis the second mistake of acquisition to the second process video for use
The corresponding user experience data of journey video.
Step S1 includes in implementation process of the present invention, using mobile phone A pp obtain user using different colours, model,
The spring spring bag process video (can be taken on site or read video file by mobile phone A pp) of pressure is transferred to cloud, in cloud
The video is resolved into continuous serial-gram by end, using face recognition technology, identifies the corresponding human face expression of the serial-gram,
Obtaining the code vector that expression changes over time, (7 kinds of expression type indignation are detested, frightened, glad, sad, surprised, loss of emotion
Corresponding code is 1,2,3,4,5,6,7), age N (year), gender B (it is 1/0 that male/female, which corresponds to code) is to the data square
Battle array makees following processing, obtains input matrix X；
Specifically, step S1 includes in an embodiment of the present invention,
S11: the two-dimentional expression spectrum that expression code vector changes over time is drawn, wherein abscissa is the time, and ordinate is
Expression type code 1-7, obtaining " indignation " corresponding expression vector is [0,0,0,0,0,0,1]T, " detest " corresponding expression to
Amount is [0,0,0,0,0,2,0]T, " fear " corresponding expression vector be [0,0,0,0,3,0,0]T, " happiness " corresponding expression to
Amount is [0,0,0,4,0,0,0]T, " sad " corresponding expression vector be [0,0,5,0,0,0,0]T, " surprised " corresponding expression to
Amount is [0,6,0,0,0,0,0]T, " loss of emotion " corresponding expression vector be [7,0,0,0,0,0,0]T；It composes to obtain square using expression
Battle array A=[e1,e2,e3,…,en]7×n(enFor one of seven kinds of expression vectors).For example, as n=10, E=[5,7,6,6,4,4,
4,4,6,7]；The expression of expression code matrices at any time is drawn to compose as shown in figure 3, being composed to obtain expression spectrum matrix A by expression:
S12: matrix A progress transposed transform is obtained into AT=[e1,e2,e3,…,en]n×7；
S13: constructing new matrix is M=AAT；
S14: calculating the characteristic value of matrix M, and value indicative matrix is λ=[λ1,λ2,λ3,…,λ7]1×7；
S15: input parameter matrix is by matrix exgenvalue, gender, age composition X=[λ, N, B]1×9。
Step S2 includes in implementation process of the present invention, the real user experience of investigation user's video process, selection point
Number 1 divides, 2 points, 3 points, one of 4 points, 5 points (it is very poor, poor, general, good, fine to respectively correspond experience of the process) as experience test knot
Fruit, and as output result y；Using a large amount of input matrix X and corresponding output matrix of consequence Y, carried out using ELMAN neural network
Modeling, ELMAN are constituted in structure by four layers, respectively input layer, mode layer, summation layer and output layer.Input layer
Number be equal to learning sample in input vector dimension, each neuron is simple distribution unit, directly passes input variable
Pass mode layer.Mode layer neuron number is equal to the number n of learning sample, and each neuron corresponds to different samples.Summation layer
It is middle to be summed using two types neuron.Neuron number in output layer is equal to the dimension of output vector in learning sample
The output for layer of summing is divided by by k, each neuron, and the output through first j corresponds to j-th of element of estimated result Y (X).
In implementation process of the present invention, in step S2, X is setk=[xk1,xk2,…,xkM] (k=1,2 ..., S) it is input
Vector, S are training sample number, WMI(g) be the g times iteration when input layer M and hidden layer I between weighted vector, WJP(g) it is
Weighted vector when the g times iteration between hidden layer J and output layer P, WJC(g) be the g time iteration when hidden layer J and undertaking layer C between
Weighted vector, Yk(g)=[yk1(g),yk2(g),…,ykP(g)] reality of network when (k=1,2 ..., S) is the g times iteration
Output, dk=[dk1,dk2,…,dkP] (k=1,2 ..., S) it is desired output.
Step S2 includes the following steps in implementation process of the present invention,
S21: initialization is assigned to W if the number of iterations g initial value is 0 respectivelyMI(0)、WJP(0)WJC(0) (0,1) section
Random value；
S22: stochastic inputs sample Xk；
S23: to input sample Xk, the input signal and output signal of every layer of neuron of forward calculation neural network；
S24: according to desired output dkWith reality output Yk(g), error E (g) is calculated；
Whether S25: error in judgement E (g) meet the requirements, and is such as unsatisfactory for, then enters step S26, such as meets, then enters step
S29；
S26: judging whether the number of iterations g+1 is greater than maximum number of iterations, such as larger than, then enters step S29, otherwise, into
Enter step S27；
S27: to input sample XkThe partial gradient δ of every layer of neuron of retrospectively calculate；
S28: modified weight amount Δ W is calculated, and corrects weight；G=g+1 is enabled, go to step S23；
S29: judging whether to complete all training samples, if it is, completing modeling, otherwise, continues to go to step
S22。
In implementation process of the present invention, step S3 includes that above-mentioned trained ELMAN model is put into cloud, which is opened
Send out into software；For newly developed spring spring bag, as long as typing video can automatically obtain the user experience of the user experience process
Evaluation result carries out product up-gradation optimum results to company and evaluates.
It should be pointed out that the above description is not a limitation of the present invention, the present invention is also not limited to the example above,
Variation, modification, addition or the replacement that those skilled in the art are made within the essential scope of the present invention, are also answered
It belongs to the scope of protection of the present invention.
Claims (10)
1. a kind of spring spring bag body based on ELMAN neural network tests evaluation method, which is characterized in that include the following steps
S1: acquisition user uses the first process video of spring spring bag, obtains the first process families according to first process video
Photo carries out recognition of face to the first process families photo and obtains user's human face expression vector, according to user's face
Expression vector obtains input matrix；
S2: acquisition user investigation data obtain matrix of consequence Y according to the user investigation data, construct ELMAN model, use
The input matrix and the matrix of consequence are trained ELMAN model.
S3: acquisition user uses the second process video of spring spring bag, is used using the ELMAN model that training is completed the user
Second process video of spring spring bag is analyzed and obtains user experience data.
2. a kind of spring spring bag body based on ELMAN neural network as described in claim 1 tests evaluation method, which is characterized in that
The step S1 includes,
S11: using abscissa as the time, ordinate is that expression type code generates user's human face expression vector changes over time two
Dimension table feelings spectrum, wherein " indignation " corresponding expression vector is [0,0,0,0,0,0,1]T, " detest " corresponding expression vector be
[0,0,0,0,0,2,0]T, " fear " corresponding expression vector be [0,0,0,0,3,0,0]T, " happiness " corresponding expression vector be
[0,0,0,4,0,0,0]T, " sad " corresponding expression vector be [0,0,5,0,0,0,0]T, " surprised " corresponding expression vector be
[0,6,0,0,0,0,0]T, " loss of emotion " corresponding expression vector be [7,0,0,0,0,0,0]T, compose to obtain matrix A using expression
=[e1,e2,e3,…,en]7×n；
S12: matrix A progress transposed transform is obtained into AT=[e1,e2,e3,…,en]n×7；
S13: structural matrix M=AAT；
S14: the characteristic value of calculating matrix M, eigenvalue matrix λ=[λ of generator matrix M1,λ2,λ3,…,λ7]1×7；
S15: it generates input matrix X=[λ, N, B]1×9, wherein N is the age, and B is gender.
3. a kind of spring spring bag body based on ELMAN neural network as claimed in claim 2 tests evaluation method, which is characterized in that
In step S2, X is setk=[xk1,xk2,…,xkM] (k=1,2 ..., S) be input vector, S be training sample number, WMI(g)
Weighted vector when for the g times iteration between input layer M and hidden layer I, WJP(g) be the g time iteration when hidden layer J and output layer P it
Between weighted vector, WJC(g) be the g time iteration when hidden layer J and undertaking layer C between weighted vector, Yk(g)=[yk1(g),yk2
(g),…,ykP(g)] reality output of network, d when (k=1,2 ..., S) is the g times iterationk=[dk1,dk2,…,dkP] (k=
1,2 ..., S) it is desired output.
4. a kind of spring spring bag body based on ELMAN neural network as claimed in claim 3 tests evaluation method, which is characterized in that
The step S2 includes the following steps
S21: initialization is assigned to W if the number of iterations g initial value is 0 respectivelyMI(0)、WJP(0)WJC(0) (0,1) section with
Machine value；
S22: stochastic inputs sample Xk；
S23: to input sample Xk, the input signal and output signal of every layer of neuron of forward calculation neural network；
S24: according to desired output dkWith reality output Yk(g), error E (g) is calculated；
Whether S25: error in judgement E (g) meet the requirements, and is such as unsatisfactory for, then enters step S26, such as meets, then enters step S29；
S26: judging whether the number of iterations g+1 is greater than maximum number of iterations, such as larger than, then enters step S29, otherwise, into step
Rapid S27；
S27: to input sample XkThe partial gradient δ of every layer of neuron of retrospectively calculate；
S28: modified weight amount Δ W is calculated, and corrects weight；G=g+1 is enabled, go to step S23；
S29: judging whether to complete all training samples, if it is, completing modeling, otherwise, continues the S22 that gos to step.
5. a kind of spring spring bag body based on ELMAN neural network as claimed in claim 4 tests evaluation method, which is characterized in that
The step S3 further includes,
User experience data is sent to administrator's mobile terminal and is shown.
6. a kind of spring spring bag body based on ELMAN neural network tests evaluation system, which is characterized in that comprise the following modules acquisition mould
Block uses the first process video of spring spring bag for acquiring user, obtains the first process families according to first process video
Photo carries out recognition of face to the first process families photo and obtains user's human face expression vector, according to user's face
Expression vector obtains input matrix,；
Training module obtains matrix of consequence Y according to the first user investigation data for the first user investigation data of acquisition,
The ELMAN model of building is trained ELMAN model using the input matrix and the matrix of consequence.
As a result output module uses the second process video of spring spring bag for acquiring user, the ELMAN model completed using training
The user is analyzed using the second process video of spring spring bag and obtains storage user experience data.
7. a kind of spring spring bag body based on ELMAN neural network as claimed in claim 6 tests evaluation system, which is characterized in that
The acquisition module obtains input matrix using following steps,
S11: using abscissa as the time, ordinate is that expression type code generates user's human face expression vector changes over time two
Dimension table feelings spectrum, wherein " indignation " corresponding expression vector is [0,0,0,0,0,0,1]T, " detest " corresponding expression vector be
[0,0,0,0,0,2,0]T, " fear " corresponding expression vector be [0,0,0,0,3,0,0]T, " happiness " corresponding expression vector be
[0,0,0,4,0,0,0]T, " sad " corresponding expression vector be [0,0,5,0,0,0,0]T, " surprised " corresponding expression vector be
[0,6,0,0,0,0,0]T, " loss of emotion " corresponding expression vector be [7,0,0,0,0,0,0]T, compose to obtain matrix A using expression
=[e1,e2,e3,…,en]7×n；
S12: matrix A progress transposed transform is obtained into AT=[e1,e2,e3,…,en]n×7；
S13: structural matrix M=AAT；
S14: the characteristic value of calculating matrix M, eigenvalue matrix λ=[λ of generator matrix M1,λ2,λ3,…,λ7]1×7；
S15: it generates input matrix X=[λ, N, B]1×9, wherein N is the age, and B is gender.
8. a kind of spring spring bag body based on ELMAN neural network as claimed in claim 7 tests evaluation system, which is characterized in that
X is arranged in the training modulek=[xk1,xk2,…,xkM] (k=1,2 ..., S) be input vector, S be training sample number, WMI
(g) be the g times iteration when input layer M and hidden layer I between weighted vector, WJP(g) hidden layer J and output layer P when being the g time iteration
Between weighted vector, WJC(g) be the g time iteration when hidden layer J and undertaking layer C between weighted vector, Yk(g)=[yk1(g),
yk2(g),…,ykP(g)] reality output of network, d when (k=1,2 ..., S) is the g times iterationk=[dk1,dk2,…,dkP](k
=1,2 ..., S) it is desired output.
9. a kind of spring spring bag body based on ELMAN neural network as claimed in claim 8 tests evaluation system, which is characterized in that
The training module is trained ELMAN model using following steps:
S21: initialization is assigned to W if the number of iterations g initial value is 0 respectivelyMI(0)、WJP(0)WJC(0) (0,1) section with
Machine value；
S22: stochastic inputs sample Xk；
S23: to input sample Xk, the input signal and output signal of every layer of neuron of forward calculation neural network；
S24: according to desired output dkWith reality output Yk(g), error E (g) is calculated；
Whether S25: error in judgement E (g) meet the requirements, and is such as unsatisfactory for, then enters step S26, such as meets, then enters step S29；
S26: judging whether the number of iterations g+1 is greater than maximum number of iterations, such as larger than, then enters step S29, otherwise, into step
Rapid S27；
S27: to input sample XkThe partial gradient δ of every layer of neuron of retrospectively calculate；
S28: modified weight amount Δ W is calculated, and corrects weight；G=g+1 is enabled, go to step S23；
S29: judging whether to complete all training samples, if it is, completing modeling, otherwise, continues the S22 that gos to step.
10. a kind of spring spring bag body based on ELMAN neural network as claimed in claim 9 tests evaluation system, which is characterized in that
The result output module is also used to, and user experience data is sent to administrator's mobile terminal and is shown.
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