CN108958037A - WAVELET FUZZY brain emotion learning control method, device, equipment and storage medium - Google Patents
WAVELET FUZZY brain emotion learning control method, device, equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a kind of WAVELET FUZZY brain emotion learning control method, device, terminal device and storage medium, method includes: acquisition input variable；Input variable is mapped by wavelet function, obtains fuzzy set.According to the learning process for the feeling and emotion for pre-establishing fuzzy rule simulation brain, the fuzzy weighted values of amygdaloid nucleus system and the fuzzy weighted values of brain prefrontal lobe system are updated by adaptive learning rule and supervised learning mode.The defuzzification operator of amygdaloid nucleus system is obtained according to the linear relationship of the fuzzy weighted values of amygdaloid nucleus system and fuzzy set and obtains the defuzzification operator of brain prefrontal lobe system according to the fuzzy weighted values of brain prefrontal lobe system and the linear relationship of fuzzy set.According to the defuzzification operator of amygdaloid nucleus system and the defuzzification operator of brain prefrontal lobe system, obtain defuzzification output result, it is exported according to defuzzification as a result, obtaining the analog result of brain emotion learning Controlling model and being simulated for the control to practical things.
Description
Technical field
The present invention relates to the identification of the nonlinear system with uncertain feature and control fields, more particularly to one
Kind WAVELET FUZZY brain emotion learning control method, device, equipment and storage medium.
Background technique
In recent years, fuzzy system and neural network have been widely used in the identification and control of nonlinear system.It is fuzzy
System can describe and handle ambiguity present in the language and thought of people, neural network can simulate human brain physiological structure and
Information process, apish intelligence are their common objectives and cooperation.But uncertain problem is obscured
Language description is limited, to the acquisition of uncertain system information be usually limited with it is incomplete, convergence and accuracy need
It further increases and existing neural network model has ignored the emotional factor that human brain learns, there are one-sidedness.
Summary of the invention
The present invention proposes a kind of WAVELET FUZZY brain emotion learning control method, device, equipment and storage medium, this hair
It is bright that traditional cerebral nerve network controller is reconstructed using wavelet function and fuzzy neural network, utilize cerebral nerve
The advantages of network controller, wavelet function and fuzzy neural network, improves the study control of traditional cerebral nerve network controller
Ability processed.
In a first aspect, the embodiment of the present invention provides a kind of WAVELET FUZZY brain emotion learning control method, specifically include:
Obtain input variable；
The input variable is mapped by wavelet function, obtains fuzzy set；
According to the learning process of the feeling for the fuzzy rule simulation brain established and emotion, advised by adaptive learning
Rule and supervised learning mode update the fuzzy weighted values of amygdaloid nucleus system and the fuzzy weighted values of brain prefrontal lobe system；
Amygdaloid nucleus system is obtained according to the linear relationship of the fuzzy weighted values of the amygdaloid nucleus system and the fuzzy set
It defuzzification operator and is obtained according to the fuzzy weighted values of the brain prefrontal lobe system and the linear relationship of the fuzzy set
The defuzzification operator of brain prefrontal lobe system；
According to the defuzzification operator of the amygdaloid nucleus system and the defuzzification operator of the brain prefrontal lobe system,
Defuzzification output is obtained as a result, being exported according to the defuzzification as a result, obtaining the analog result of brain emotion learning model
And for the simulation to practical things.
Further, the fuzzy rule of the WAVELET FUZZY brain emotion learning controller pre-established are as follows: amygdaloid nucleus system
System:thenuao=vio(for i=1,2 ..., ni, o=1,2 ..., no) and
Brain prefrontal lobe systemthen upo=wio(for i=1,2 ..., ni, o=1,
2,...,no)；In the sense of the fuzzy rule simulation brain for the WAVELET FUZZY brain emotion learning controller that the basis pre-establishes
Feel the learning process with emotion, the fuzzy weighted values of amygdaloid nucleus system are updated by adaptive learning rule and supervised learning mode
Before the fuzzy weighted values step of brain prefrontal lobe system, comprising: preset the initial fuzzy weighted values, initial of amygdaloid nucleus system
The initial fuzzy weighted values of learning rate and mood signal adjusting parameter and brain prefrontal lobe system, initial learning rate and mood signal
Adjusting parameter.
Further, the defuzzification operator calculation formula of the amygdaloid nucleus system are as follows:Brain forehead
Leaf system system defuzzification operator calculation formula beWherein, niIt is input dimension, siIt is i-th of input,
vioIt is the weighted value of the amygdaloid nucleus fuzzy set of o-th of output, wioThe power of the brain prefrontal lobe fuzzy set of o-th of output
Weight values.
Further, according to the defuzzification operator of the amygdaloid nucleus system and the solution mould of the brain prefrontal lobe system
It is gelatinized operator, obtains the calculation formula of the output result are as follows: yo=ao-po。
Second aspect, the embodiment of the present invention provide a kind of WAVELET FUZZY brain emotion learning control device, specifically include:
Module is obtained, for obtaining input variable.
Mapping block obtains fuzzy set for mapping the input variable by wavelet function；
Update module, for passing through according to the feeling for the fuzzy rule simulation brain established and the learning process of emotion
Adaptive learning rule and supervised learning mode update the fuzzy weighted values of amygdaloid nucleus system and the fuzzy weight of brain prefrontal lobe system
Weight.
Computing module, for being obtained according to the fuzzy weighted values of the amygdaloid nucleus system and the linear relationship of the fuzzy set
Fuzzy weighted values and the fuzzy set to the defuzzification operator of amygdaloid nucleus system and according to the brain prefrontal lobe system
Linear relationship obtain the defuzzification operator of brain prefrontal lobe system.
Output module, for according to the defuzzification operator of the amygdaloid nucleus system and the brain prefrontal lobe system
Defuzzification operator obtains defuzzification output as a result, being exported according to the defuzzification as a result, obtaining brain emotion learning
The analog result of model and for simulation to practical things.
Further, comprising:
Setup module, for presetting the initial fuzzy weighted values, initial learning rate and mood signal tune of amygdaloid nucleus system
The initial fuzzy weighted values of whole parameter and brain prefrontal lobe system, initial learning rate and mood signal adjusting parameter.
Further, the defuzzification operator calculation formula of the amygdaloid nucleus system are as follows:Brain forehead
Leaf system system defuzzification operator calculation formula beWherein, niIt is input dimension, siIt is i-th of input,
vioIt is the weighted value of the amygdaloid nucleus fuzzy set of o-th of output, wioThe power of the brain prefrontal lobe fuzzy set of o-th of output
Weight values.
Further, according to the defuzzification operator of the amygdaloid nucleus system and the solution mould of the brain prefrontal lobe system
It is gelatinized operator, obtains the calculation formula of the output result are as follows: yo=ao-po。
The third aspect, the embodiment of the present invention provide a kind of terminal device, including processor, memory and are stored in institute
It states in memory and is configured the computer program executed by the processing, when the processor executes the computer program
Realize a kind of WAVELET FUZZY brain emotion learning control method as described in relation to the first aspect.
Fourth aspect, the embodiment of the present invention provide a kind of computer readable storage medium, which is characterized in that the calculating
Machine readable storage medium storing program for executing includes the computer program of storage, wherein controls the computer in computer program operation
Equipment executes WAVELET FUZZY brain emotion learning control method as described in relation to the first aspect where readable storage medium storing program for executing.
The implementation of the embodiments of the present invention has the following beneficial effects:
1, by simulation human brain learning process, wavelet function is combined with fuzzy brain emotion learning controller, is mentioned
It is more suitable for the fuzzy rule base of human brain learning mechanic out, constructs the more perfect WAVELET FUZZY brain emotion learning control of new performance
Device structure processed has the advantages that wavelet function, fuzzy inference system and brain emotion neural network.
2, present invention tool is there are two fuzzy rule system (amygdaloid nucleus fuzzy system and brain prefrontal lobe fuzzy system), can be with
The expression for simulating the feeling and emotion of brain increases the study to emotion compared to conventional fuzzy neural network, can be in more detail
The complicated uncertain non-stationary signal of ground description.
3, by the automatic adjusument to parameter, model adaptation ability is improved, obtains more quick convergence and more
Add good stability.
Detailed description of the invention
It, below will be to attached drawing needed in embodiment in order to illustrate more clearly of technical solution of the present invention
It is briefly described, it should be apparent that, the accompanying drawings in the following description is only some embodiments of the present invention, for ability
For the those of ordinary skill of domain, without creative efforts, it can also be obtained according to these attached drawings other attached
Figure.
Fig. 1 is the structure chart of WAVELET FUZZY brain emotion learning controller provided by the invention.
Fig. 2 is the flow diagram for the WAVELET FUZZY brain emotion learning control method that first embodiment of the invention provides.
Fig. 3 is the structural schematic diagram for the WAVELET FUZZY brain emotion learning control device that second embodiment of the invention provides.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts it is all its
His embodiment, shall fall within the protection scope of the present invention.
Referring to Fig. 1, Fig. 1 is the structure chart of WAVELET FUZZY brain emotion learning controller provided by the invention, and the present invention mentions
Confession WAVELET FUZZY brain emotion learning controller (Wavelet Fuzzy Brain Emotional Learning Control,
It WFBELC), is a mathematical model of the brain to objective things description, learning process of a simulation people.WAVELET FUZZY brain feelings
Sense learning controller is made of 5 spaces:
1, the input space is made of input variable.
2, memory space is that basic function is constituted by basic function of wavelet function.By this space, input variable is reflected
It is mapped in the fuzzy set of brain emotion learning controller.
3, weight space, the space include the fuzzy weighted values of amygdaloid nucleus system and the fuzzy weighted values of brain prefrontal lobe system.
4, degradation space, the process of drop type and defuzzification.According to linear changing relation, the solution of amygdaloid nucleus system is obtained
It is blurred the defuzzification operator of operator and brain prefrontal lobe system.
5, space, the output of WAVELET FUZZY brain emotion learning controller are exported.
First embodiment of the invention:
Referring to fig. 2, Fig. 2 is the stream for the WAVELET FUZZY brain emotion learning control method that first embodiment of the invention provides
Journey schematic diagram.The embodiment of the present invention provides a kind of WAVELET FUZZY brain emotion learning control method, specifically includes the following steps:
S10 obtains input variable.
Obtain the input variable of amygdaloid nucleus fuzzy system and brain prefrontal lobe fuzzy system
The input signal of the corresponding outside for inputing to model each input variable Ii, with the fuzzy rule inside the mathematical model
Linguistic variable then is also corresponding.
The input variable is mapped by wavelet function, obtains fuzzy set by S20
It is constituted by basic function of wavelet function through memory space, input variable is mapped to the control of brain emotion learning
In the fuzzy set of device:The fuzzy set is used for WAVELET FUZZY brain emotion learning controller mould
Inside type, when being applied to real system progress Fuzzy processing according to the fuzzy theory.
S30, according to the fuzzy rule of the WAVELET FUZZY brain emotion learning controller pre-established simulate brain feeling and
The learning process of emotion updates the fuzzy weighted values and brain of amygdaloid nucleus system by adaptive learning rule and supervised learning mode
The fuzzy weighted values of prefrontal lobe system.
Preferably, the initialization of self study process needs to carry out partial parameters initial setting up, described to pre-establish
The fuzzy rule of WAVELET FUZZY brain emotion learning controller are as follows: amygdaloid nucleus system:
thenuao=vio(for i=1,2 ..., ni, o=1,2 ..., no) and brain prefrontal lobe systemthen upo=wio(for i=1,2 ..., ni, o=1,2 ..., no).?
The feeling of brain and the learning process of emotion are simulated according to the fuzzy rule for the WAVELET FUZZY emotion learning controller established,
The fuzzy weighted values of amygdaloid nucleus system and obscuring for brain prefrontal lobe system are updated by adaptive learning rule and supervised learning mode
Before weight step, comprising: preset initial fuzzy weighted values, initial learning rate and mood the signal adjustment of amygdaloid nucleus system
The initial fuzzy weighted values of parameter and brain prefrontal lobe system, initial learning rate and mood signal adjusting parameter.Wherein, amygdaloid nucleus
The fuzzy weighted values of system are as follows:The fuzzy weighted values of brain prefrontal lobe system
Are as follows:
Brain emotional learning process is by updating weight vioAnd wioIt realizes.According to the physiology of human brain emotional learning
Process, amygdaloid nucleus system and brain prefrontal lobe system are adjusted according to following parameter adaptive learning law:WithWherein: ηvAnd ηwIt is learning rate, d0It is mood letter
Number adjusting parameter.Then adaptive supervised learning formula are as follows: vio(k+1)=vio(k)+Δvio(k) and wio(k+1)=wio(k)
+Δwio(k)。
S40 obtains the solution mould of amygdaloid nucleus system according to the linear relationship of the fuzzy weighted values of amygdaloid nucleus system and fuzzy set
It is gelatinized operator and obtains brain prefrontal lobe system according to the fuzzy weighted values of brain prefrontal lobe system and the linear relationship of fuzzy set
Defuzzification operator.
The process of drop type and defuzzification.According to linear changing relation, the defuzzification operator of amygdaloid nucleus system is obtainedWith the defuzzification operator of brain prefrontal lobe systemThe effect of blurring is to pass through mould
Theoretical application is pasted, makes the mathematical modeling established to system closer to real system, but it does not have the ability of study；Institute
After increasing upper adaptive learning, entire model is enable preferably to approach practical things.
S50 is obtained according to the defuzzification operator of amygdaloid nucleus system and the defuzzification operator of brain prefrontal lobe system
Defuzzification output as a result, according to defuzzification export as a result, obtain brain emotion learning model analog result and for pair
The simulation of practical things.
The output y of WAVELET FUZZY brain emotion learning controllero=ao-po, also as model is after self-supervisory learns
As a result, being allowed to more approach practical things by training and study to model.Preferably, during self study, according to self-study
" stability and convergence | " condition of habit process, it is ensured that WAVELET FUZZY brain emotion learning controller model obtains more quick
Convergence and more good stability.The stability and convergence condition is specially as follows:
Consider the multi-input multi-output system of a nonlinear uncertain
Wherein:It is a unknown indeterminate.
Definition: a synovial membrane planeWherein: e is system tracking error.
Definition: 0≤| | ε | |1≤ D, in which: ε is the approximate error of a bounded, and D is a positive constant,It is to estimate
Measured value,
Then,It is positive semidefinite function
Then,
Definition: Ψ ≡ (D- | | ε | |1) s, it can obtain:
Work as limt→∞The WAVELET FUZZY brain emotion learning controller of Ψ (t)=0 (WFBELC) is asymptotically stable.And t →
The tracking error of ∞, uncertain nonlinear system will quickly and accurately approach 0.In the automatic adjusument process for having supervision
In, it is ensured that WAVELET FUZZY brain emotion learning controller obtains more quick convergence and more good stability.
Second embodiment of the invention:
Referring to Fig. 3, Fig. 3 is the knot for the WAVELET FUZZY brain emotion learning control device that second embodiment of the invention provides
Structure schematic diagram.The embodiment of the present invention provides a kind of WAVELET FUZZY brain emotion learning control device, specifically includes:
Module 100 is obtained, for obtaining input variable.
Mapping block 200 obtains fuzzy set for mapping the input variable by wavelet function；
Update module 300 simulates brain according to the fuzzy rule of the WAVELET FUZZY brain emotion learning controller pre-established
Feeling and emotion learning process, the fuzzy of amygdaloid nucleus system is updated by adaptive learning rule and supervised learning mode
The fuzzy weighted values of weight and brain prefrontal lobe system；
Computing module 400, for according to the fuzzy weighted values of the amygdaloid nucleus system and the linear relationship of the fuzzy set
Obtain the defuzzification operator of amygdaloid nucleus system and fuzzy weighted values and the fuzzy set according to the brain prefrontal lobe system
The linear relationship of conjunction obtains the defuzzification operator of brain prefrontal lobe system；
Output module 500, for according to the amygdaloid nucleus system defuzzification operator and the brain prefrontal lobe system
Defuzzification operator, obtain defuzzification output as a result, according to the defuzzification export as a result, obtain brain sentics
Practise the analog result of model and for the simulation to practical things.
Preferably, further includes: setup module 600, for presetting the initial fuzzy weighted values, initial of amygdaloid nucleus system
The initial fuzzy weighted values of learning rate and mood signal adjusting parameter and brain prefrontal lobe system, initial learning rate and mood signal
Adjusting parameter.
The computing module is also used to the defuzzification operator calculation formula of the amygdaloid nucleus system are as follows:The defuzzification operator calculation formula of brain prefrontal lobe system isWherein, niIt is input dimension
Number, siIt is i-th of input, vioIt is the weighted value of the amygdaloid nucleus fuzzy set of o-th of output, wioBefore the brain of o-th of output
The weighted value of frontal lobe fuzzy set.
It is also used to the defuzzification of the defuzzification operator and the brain prefrontal lobe system according to the amygdaloid nucleus system
Operator obtains the calculation formula of the output result are as follows: yo=ao-po。
The third embodiment of the present invention:
Third embodiment of the invention provides a kind of terminal device, including processor, memory and is stored in the storage
In device and it is configured the computer program executed by the processing.The processor is realized when executing the computer program
State the step in a kind of described in any item WAVELET FUZZY brain emotion learning control method embodiments, such as step shown in Fig. 2
Rapid S10.Alternatively, the processor realizes the function in above-mentioned each device example, such as Fig. 3 when executing the computer program
Shown in acquisition module 100.
Fourth embodiment of the invention:
Fourth embodiment of the invention provides a kind of computer readable storage medium, the computer readable storage medium packet
Include the computer program of storage, such as a kind of program of WAVELET FUZZY brain emotion learning control method.Wherein, in the meter
Equipment calculation machine program controls the computer readable storage medium when running where executes one described in above-mentioned first embodiment
Kind WAVELET FUZZY brain emotion learning control method.
Illustratively, computer program described in third embodiment of the invention and fourth embodiment can be divided into
One or more modules, one or more of modules are stored in the memory, and are executed by the processor,
To complete the present invention.One or more of modules can be the series of computation machine program instruction that can complete specific function
Section, the instruction segment control equipment in the realization WAVELET FUZZY brain emotion learning for describing the computer program, specifically
Implementation procedure in including the following steps.For example, device described in the embodiment of the present invention two.
Alleged processor can be central processing unit (Central Processing Unit, CPU), can also be it
His general processor, digital signal processor (Digital Signal Processor, DSP), specific integrated circuit
(Application Specific Integrated Circuit, ASIC), ready-made programmable gate array (Field-
Programmable Gate Array, FPGA) either other programmable logic device, discrete gate or transistor logic device
Part, discrete hardware components etc..General processor can be microprocessor or the processor is also possible to any conventional processing
Device etc., the processor are the control centres of the WAVELET FUZZY brain emotion learning control method, utilize various interfaces and line
The entire various pieces for realizing WAVELET FUZZY brain emotion learning control method of road connection.
The memory can be used for storing the computer program and/or module, and the processor is by operation or executes
Computer program in the memory and/or module are stored, and calls the data being stored in memory, is realized small
Wave obscures the various functions of brain emotion learning control method.The memory can mainly include storing program area and storage number
According to area, wherein storing program area can application program needed for storage program area, at least one function (for example sound plays function
Energy, text conversion function etc.) etc.；Storage data area, which can be stored, uses created data (such as audio number according to mobile phone
According to, text message data etc.) etc..In addition, memory may include high-speed random access memory, it can also include non-volatile
Property memory, such as hard disk, memory, plug-in type hard disk, intelligent memory card (Smart Media Card, SMC), secure digital
(Secure Digital, SD) card, flash card (Flash Card), at least one disk memory, flush memory device or its
His volatile solid-state part.
Wherein, the module for realizing the identification to the nonlinear system with uncertain feature and control device is such as
Fruit is realized in the form of SFU software functional unit and when sold or used as an independent product, can store in a computer
In read/write memory medium.Based on this understanding, the present invention realizes all or part of the process in above-described embodiment method,
Relevant hardware can also be instructed to complete by computer program, the computer program can be stored in a calculating
In machine readable storage medium storing program for executing, the computer program is when being executed by processor, it can be achieved that the step of above-mentioned each embodiment of the method
Suddenly.Wherein, the computer program includes computer program code, the computer program code can for source code form,
Object identification code form, executable file or certain intermediate forms etc..The computer-readable medium may include: that can carry
Any entity or device, recording medium, USB flash disk, mobile hard disk, magnetic disk, CD, the computer of the computer program code are deposited
Reservoir, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access
Memory), electric carrier signal, telecommunication signal and software distribution medium etc..It should be noted that computer-readable Jie
The content that matter includes can carry out increase and decrease appropriate according to the requirement made laws in jurisdiction with patent practice, such as at certain
A little jurisdictions do not include electric carrier signal and telecommunication signal according to legislation and patent practice, computer-readable medium.
It should be noted that the apparatus embodiments described above are merely exemplary, wherein described be used as separation unit
The unit of explanation may or may not be physically separated, and component shown as a unit can be or can also
Not to be physical unit, it can it is in one place, or may be distributed over multiple network units.It can be according to reality
Border needs to select some or all of the modules therein to achieve the purpose of the solution of this embodiment.In addition, provided by the invention
In Installation practice attached drawing, the connection relationship between module indicates there is communication connection between them, specifically can be implemented as one
Item or a plurality of communication bus or signal wire.Those of ordinary skill in the art are without creative efforts, it can
It understands and implements.
The above is a preferred embodiment of the present invention, it is noted that for those skilled in the art
For, various improvements and modifications may be made without departing from the principle of the present invention, these improvements and modifications also regard
For protection scope of the present invention.
Claims (10)
1. a kind of WAVELET FUZZY brain emotion learning control method, which is characterized in that the described method includes:
Obtain input variable；
The input variable is mapped by wavelet function, obtains fuzzy set；
The feeling and emotion of brain is simulated according to the fuzzy rule of the WAVELET FUZZY brain emotion learning controller pre-established
Habit process updates the fuzzy weighted values and brain prefrontal lobe system of amygdaloid nucleus system by adaptive learning rule and supervised learning mode
Fuzzy weighted values；
The solution mould of amygdaloid nucleus system is obtained according to the linear relationship of the fuzzy weighted values of the amygdaloid nucleus system and the fuzzy set
Before being gelatinized operator and obtaining brain according to the fuzzy weighted values of the brain prefrontal lobe system and the linear relationship of the fuzzy set
The defuzzification operator of frontal lobe system；
According to the defuzzification operator of the amygdaloid nucleus system and the defuzzification operator of the brain prefrontal lobe system, solved
Blurring output according to the defuzzification as a result, export as a result, obtaining the analog result of brain emotion learning Controlling model simultaneously
For the simulation to practical things.
2. WAVELET FUZZY brain emotion learning control method according to claim 1, which is characterized in that described to pre-establish
WAVELET FUZZY brain emotion learning controller fuzzy rule are as follows: amygdaloid nucleus system:
thenuao=vio(fori=1,2 ..., ni, o=1,2 ..., no) and brain prefrontal lobe system
thenupo=wio(fori=1,2 ..., ni, o=1,2 ..., no)；In the WAVELET FUZZY brain emotion that the basis pre-establishes
The feeling of the fuzzy rule simulation brain of learning controller and the learning process of emotion, are learned by adaptive learning rule and supervision
Habit mode updates before the fuzzy weighted values of amygdaloid nucleus system and the fuzzy weighted values step of brain prefrontal lobe system, comprising: presets
Initial fuzzy weighted values, initial learning rate and the mood signal adjusting parameter of amygdaloid nucleus system and the introductory die of brain prefrontal lobe system
Paste weight, initial learning rate and mood signal adjusting parameter.
3. WAVELET FUZZY brain emotion learning control method according to claim 1, which is characterized in that the amygdaloid nucleus system
The defuzzification operator calculation formula of system are as follows:The defuzzification operator calculation formula of brain prefrontal lobe system isWherein, niIt is input dimension, siIt is i-th of input, vioIt is the amygdaloid nucleus fuzzy set of o-th of output
Weighted value, wioThe weighted value of the brain prefrontal lobe fuzzy set of o-th of output.
4. WAVELET FUZZY brain emotion learning control method according to claim 1, which is characterized in that
According to the defuzzification operator of the amygdaloid nucleus system and the defuzzification operator of the brain prefrontal lobe system, institute is obtained
State the calculation formula of output result are as follows: yo=ao-po。
5. a kind of WAVELET FUZZY brain emotion learning control device characterized by comprising
Module is obtained, for obtaining input variable；Mapping block, for being reflected the input variable by wavelet function
It penetrates, obtains fuzzy set；
Update module simulates the sense of brain for the fuzzy rule according to the WAVELET FUZZY brain emotion learning controller pre-established
Feel and emotion learning process, by adaptive learning rule and supervised learning mode update amygdaloid nucleus system fuzzy weighted values and
The fuzzy weighted values of brain prefrontal lobe system；
Computing module, for obtaining almond according to the fuzzy weighted values of the amygdaloid nucleus system and the linear relationship of the fuzzy set
The defuzzification operator of core system and according to the linear of the fuzzy weighted values of the brain prefrontal lobe system and the fuzzy set
Relationship obtains the defuzzification operator of brain prefrontal lobe system；
Output module, for according to the defuzzification operator of the amygdaloid nucleus system and the ambiguity solution of the brain prefrontal lobe system
Change operator, obtains defuzzification output as a result, being exported according to the defuzzification as a result, obtaining the mould of brain emotion learning model
Intend result and for the simulation to practical things.
6. WAVELET FUZZY brain emotion learning control method according to claim 5 characterized by comprising
Setup module, initial fuzzy weighted values, initial learning rate and mood signal for presetting amygdaloid nucleus system adjust ginseng
Several and brain prefrontal lobe system initial fuzzy weighted values, initial learning rate and mood signal adjusting parameter.
7. WAVELET FUZZY brain emotion learning control device according to claim 5, which is characterized in that the amygdaloid nucleus system
The defuzzification operator calculation formula of system are as follows:The defuzzification operator calculation formula of brain prefrontal lobe system isWherein, niIt is input dimension, siIt is i-th of input, vioIt is the amygdaloid nucleus fuzzy set of o-th of output
Weighted value, wioThe weighted value of the brain prefrontal lobe fuzzy set of o-th of output.
8. WAVELET FUZZY brain emotion learning control device according to claim 5, which is characterized in that according to the almond
The defuzzification operator of the defuzzification operator of core system and the brain prefrontal lobe system obtains the calculating of the output result
Formula are as follows: yo=ao-po。
9. a kind of terminal device, which is characterized in that including processor, memory and store in the memory and be configured
The computer program executed by the processing, the processor realize that Claims 1-4 such as is appointed when executing the computer program
A kind of WAVELET FUZZY brain emotion learning control method described in one.
10. a kind of computer readable storage medium, which is characterized in that the computer readable storage medium includes the calculating of storage
Machine program, wherein equipment where controlling the computer readable storage medium in computer program operation is executed as weighed
Benefit requires any one WAVELET FUZZY brain emotion learning control method in 1 to 4.
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