CN108958037B - 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
- Publication number
- CN108958037B CN108958037B CN201810927199.0A CN201810927199A CN108958037B CN 108958037 B CN108958037 B CN 108958037B CN 201810927199 A CN201810927199 A CN 201810927199A CN 108958037 B CN108958037 B CN 108958037B
- Authority
- CN
- China
- Prior art keywords
- fuzzy
- brain
- emotion
- defuzzification
- learning
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Computation (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Feedback Control In General (AREA)
Abstract
The invention discloses a wavelet fuzzy brain emotion learning control method, a device, terminal equipment and a storage medium, wherein the method comprises the following steps: acquiring an input variable; and mapping the input variable through a wavelet function to obtain a fuzzy set. Simulating the learning process of the feeling and emotion of the brain according to a pre-established fuzzy rule, and updating the fuzzy weight of an amygdala system and the fuzzy weight of a brain prefrontal lobe system through a self-adaptive learning rule and a supervised learning mode. And obtaining the defuzzification operator of the amygdala system according to the linear relation between the fuzzy weight of the amygdala system and the fuzzy set, and obtaining the defuzzification operator of the brain prefrontal leaf system according to the fuzzy weight of the brain prefrontal leaf system and the linear relation between the fuzzy set. And obtaining a defuzzification output result according to a defuzzification operator of the amygdala system and a defuzzification operator of the brain prefrontal lobe system, and obtaining a simulation result of the brain emotion learning control model according to the defuzzification output result and using the simulation result for controlling and simulating actual objects.
Description
Technical Field
The invention relates to the field of identification and control of a nonlinear system with uncertain characteristics, in particular to a wavelet fuzzy brain emotion learning control method, a device, equipment and a storage medium.
Background
In recent years, fuzzy systems and neural networks have been widely used for identification and control of nonlinear systems. The fuzzy system can describe and process the ambiguity existing in human language and thinking, the neural network can simulate the physiological structure of human brain and information processing process, and the simulation of human intelligence is the common target and cooperation basis of the fuzzy system and the neural network. However, fuzzy language description of uncertainty problems is limited, uncertain system information is generally limited and incomplete, convergence and accuracy are to be further improved, and emotional factors of human brain learning are ignored in the existing neural network model, so that one-sidedness exists.
Disclosure of Invention
The invention provides a wavelet fuzzy brain emotion learning control method, a device, equipment and a storage medium.
In a first aspect, an embodiment of the present invention provides a wavelet fuzzy brain emotion learning control method, which specifically includes:
acquiring an input variable;
mapping the input variable through a wavelet function to obtain a fuzzy set;
simulating the learning process of the feeling and emotion of the brain according to the established fuzzy rule, and updating the fuzzy weight of an amygdala system and the fuzzy weight of a brain prefrontal lobe system through a self-adaptive learning rule and a supervised learning mode;
obtaining a defuzzification operator of the amygdala system according to the fuzzy weight of the amygdala system and the linear relation of the fuzzy set, and obtaining a defuzzification operator of the brain prefrontal leaf system according to the fuzzy weight of the brain prefrontal leaf system and the linear relation of the fuzzy set;
and obtaining a defuzzification output result according to the defuzzification operator of the amygdala system and the defuzzification operator of the brain prefrontal leaf system, and obtaining a simulation result of a brain emotion learning model according to the defuzzification output result and using the simulation result for simulating actual things.
Further, the fuzzy rule of the pre-established wavelet fuzzy brain emotion learning controller is as follows: amygdala system:
and the prefrontal system of the brain
Simulating the feeling and emotion of the brain according to the fuzzy rule of the pre-established wavelet fuzzy brain emotion learning controllerThe learning process, before the step of updating the fuzzy weight of the amygdala system and the fuzzy weight of the brain prefrontal lobe system through a self-adaptive learning rule and a supervised learning mode, comprises the following steps: the initial fuzzy weight, the initial learning rate and the emotion signal adjusting parameters of the amygdala system and the initial fuzzy weight, the initial learning rate and the emotion signal adjusting parameters of the brain prefrontal lobe system are preset.
Further, the defuzzification operator of the almond kernel system has a calculation formula as follows:
the defuzzification operator of the brain prefrontal lobe system has a calculation formula of
Wherein n isiIs the input dimension, siIs the ith output, vioIs the weight value of the kernel fuzzy set of the output of the oioThe weight value of the o-th output of the brain prefrontal leaf blur set.
Further, according to the defuzzification operator of the amygdala system and the defuzzification operator of the brain prefrontal system, a calculation formula for obtaining the output result is as follows: y iso＝ao-po。
In a second aspect, an embodiment of the present invention provides a wavelet fuzzy brain emotion learning control apparatus, which specifically includes:
and the acquisition module is used for acquiring the input variable.
The mapping module is used for mapping the input variable through a wavelet function to obtain a fuzzy set;
and the updating module is used for simulating the learning process of the feeling and emotion of the brain according to the established fuzzy rule and updating the fuzzy weight of the amygdala system and the fuzzy weight of the brain prefrontal lobe system through a self-adaptive learning rule and a supervised learning mode.
And the calculation module is used for obtaining a defuzzification operator of the amygdala system according to the fuzzy weight of the amygdala system and the linear relation of the fuzzy set and obtaining the defuzzification operator of the brain prefrontal system according to the fuzzy weight of the brain prefrontal system and the linear relation of the fuzzy set.
And the output module is used for obtaining a defuzzification output result according to the defuzzification operator of the amygdala system and the defuzzification operator of the brain prefrontal leaf system, and obtaining a simulation result of a brain emotion learning model according to the defuzzification output result and simulating actual objects.
Further, comprising:
and the setting module is used for presetting the initial fuzzy weight, the initial learning rate and the emotion signal adjusting parameter of the amygdala system and the initial fuzzy weight, the initial learning rate and the emotion signal adjusting parameter of the brain prefrontal lobe system.
Further, the defuzzification operator of the almond kernel system has a calculation formula as follows:
the defuzzification operator of the brain prefrontal lobe system has a calculation formula of
Wherein n isiIs the input dimension, siIs the ith output, vioIs the weight value of the kernel fuzzy set of the output of the oioThe weight value of the o-th output of the brain prefrontal leaf blur set.
Further, according to the defuzzification operator of the amygdala system and the defuzzification operator of the brain prefrontal system, a calculation formula for obtaining the output result is as follows: y iso＝ao-po。
In a third aspect, an embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processing, and when the processor executes the computer program, the processor implements the wavelet fuzzy brain emotion learning control method according to the first aspect.
In a fourth aspect, the present invention provides a computer-readable storage medium, where the computer-readable storage medium includes a stored computer program, where the computer program, when running, controls a device in which the computer-readable storage medium is located to perform the wavelet fuzzy brain emotion learning control method according to the first aspect.
The embodiment of the invention has the following beneficial effects:
1. by simulating the learning process of the human brain, the wavelet function is combined with the fuzzy brain emotion learning controller, a fuzzy rule base more suitable for the human brain learning mechanism is provided, a new wavelet fuzzy brain emotion learning controller structure with more perfect performance is constructed, and the method has the advantages of the wavelet function, a fuzzy inference system and a brain emotion neural network.
2. The invention has two fuzzy rule systems (an amygdala fuzzy system and a brain prefrontal lobe fuzzy system), can simulate the feeling of the brain and the expression of emotion, increases the learning of emotion compared with the conventional fuzzy neural network, and can describe complex uncertain non-stationary signals in more detail.
3. By adaptive adjustment of parameters, the model adaptive capacity is improved, and faster convergence and better stability are obtained.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a structural diagram of a wavelet fuzzy brain emotion learning controller provided by the invention.
Fig. 2 is a flowchart illustrating a wavelet fuzzy brain emotion learning control method according to a first embodiment of the present invention.
Fig. 3 is a schematic structural diagram of a wavelet fuzzy brain emotion learning control device according to a second embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, fig. 1 is a structural diagram of a Wavelet Fuzzy Brain emotion Learning controller provided by the present invention, and the Wavelet Fuzzy Brain Emotion Learning Controller (WFBELC) provided by the present invention is a mathematical model simulating a human Brain to describe and learn objective objects. The wavelet fuzzy brain emotion learning controller consists of 5 spaces:
1. and the input space is composed of input variables.
2. The memory space is formed by taking a wavelet function as a basic function. Through this space, the input variables are mapped into a fuzzy set of the brain emotion learning controller.
3. A weight space comprising the fuzzy weights of the amygdala system and the fuzzy weights of the brain prefrontal system.
4. The process of space degradation, type degradation and defuzzification. And according to the linear variation relation, acquiring the defuzzification operator of the amygdala system and the defuzzification operator of the brain prefrontal leaf system.
5. And outputting the space and the output of the wavelet fuzzy brain emotion learning controller.
The first embodiment of the present invention:
referring to fig. 2, fig. 2 is a schematic flow chart of a wavelet fuzzy brain emotion learning control method according to a first embodiment of the present invention. The embodiment of the invention provides a wavelet fuzzy brain emotion learning control method, which specifically comprises the following steps:
and S10, acquiring input variables.
Obtaining input variables of an amygdala fuzzy system and a brain prefrontal leaf fuzzy system
Each input variable Ii corresponds to an external input signal to the model, as well as linguistic variables of fuzzy rules within the mathematical model.
S20, mapping the input variable through a wavelet function to obtain a fuzzy set
And (3) mapping the input variables into a fuzzy set of a brain emotion learning controller through a memory space by taking a wavelet function as a basis function:
the fuzzy set is used in a wavelet fuzzy brain emotion learning controller model and is applied to an actual system for fuzzification processing according to the fuzzy theory.
And S30, simulating the learning process of brain feeling and emotion according to the fuzzy rule of the wavelet fuzzy brain emotion learning controller established in advance, and updating the fuzzy weight of the amygdala system and the fuzzy weight of the brain prefrontal lobe system through a self-adaptive learning rule and a supervised learning mode.
Preferably, the initialization of the self-learning process requires initial setting of partial parameters, and the fuzzy rule of the pre-established wavelet fuzzy brain emotion learning controller is as follows: amygdala system:
and the prefrontal system of the brain
Before the steps of simulating the learning process of the feeling and emotion of the brain according to the fuzzy rule of the established wavelet fuzzy emotion learning controller and updating the fuzzy weight of the amygdala system and the fuzzy weight of the brain prefrontal lobe system through the self-adaptive learning rule and the supervised learning mode, the method comprises the following steps of: the initial fuzzy weight, the initial learning rate and the emotion signal adjusting parameters of the amygdala system and the initial fuzzy weight, the initial learning rate and the emotion signal adjusting parameters of the brain prefrontal lobe system are preset. Wherein the amygdala is systemicThe fuzzy weight is:
the fuzzy weight of the brain prefrontal lobe system is:
the learning process of brain emotion is realized by updating the weight vioAnd wioTo be implemented. According to the physiological process of human brain emotion learning, an amygdala system and a brain prefrontal system are adjusted according to the following parameter adaptive learning rule:
and
wherein: etavAnd ηwIs the learning rate, d0Is an emotional signal adjustment parameter. The adaptive supervised learning formula is: v. ofio(k+1)＝vio(k)+Δvio(k) And wio(k+1)＝wio(k)+Δwio(k)。
And S40, obtaining the defuzzification operator of the amygdala system according to the linear relation between the fuzzy weight of the amygdala system and the fuzzy set, and obtaining the defuzzification operator of the brain prefrontal leaf system according to the fuzzy weight of the brain prefrontal leaf system and the linear relation between the fuzzy sets.
And (3) a process of type reduction and defuzzification. According to the linear variation relation, acquiring the defuzzification operator of the almond kernel system
Defuzzification operator for the frontal brain lobe system
The fuzzification has the effect that through the application of a fuzzy theory, the mathematical modeling established on the system is closer to an actual system, but the mathematical modeling has no learning capacity; therefore, after the self-adaptive learning is added, the whole model can better approach to the realityThings are.
And S50, acquiring a defuzzification output result according to the defuzzification operator of the amygdala system and the defuzzification operator of the brain prefrontal leaf system, and acquiring a simulation result of the brain emotion learning model according to the defuzzification output result and using the simulation result for simulating actual things.
Output y of wavelet fuzzy brain emotion learning controllero＝ao-poNamely, the result of the model after the self-supervision learning is more similar to the actual object through the training and learning of the model. Preferably, in the self-learning process, according to the condition of stability and convergence | in the self-learning process, the wavelet fuzzy brain emotion learning controller model is ensured to obtain faster convergence and better stability. The stability and convergence conditions are specifically as follows:
multiple-input multiple-output system considering a non-linear uncertainty
Wherein:
is an unknown uncertainty.
Defining: a synovial plane
Wherein: e is the system tracking error.
Defining: less than or equal to | Epsilon | | non-woven phosphor powder1D or less, wherein: e is a bounded approximation error, D is a positive constant,
is an estimated value of the amount of time,
then the process of the first step is carried out,
is a semi-positive definite function
Then the process of the first step is carried out,
defining: Ψ ≡ (D- | | ε | | non-woven phosphor1) s, one can obtain:
when limt→∞Ψ (t) ═ 0 Wavelet Fuzzy Brain Emotion Learning Controller (WFBELC) is asymptotically stable. And t → ∞, the tracking error of the uncertain nonlinear system will quickly and accurately approach 0. In the process of supervised self-adaptive adjustment, the wavelet fuzzy brain emotion learning controller is ensured to obtain faster convergence and better stability.
Second embodiment of the invention:
referring to fig. 3, fig. 3 is a schematic structural diagram of a wavelet fuzzy brain emotion learning control device according to a second embodiment of the present invention. The embodiment of the invention provides a wavelet fuzzy brain emotion learning control device, which specifically comprises:
the obtaining module 100 is configured to obtain an input variable.
A mapping module 200, configured to map the input variable through a wavelet function to obtain a fuzzy set;
the updating module 300 is used for simulating the learning process of the feeling and emotion of the brain according to the fuzzy rule of the wavelet fuzzy brain emotion learning controller established in advance, and updating the fuzzy weight of the amygdala system and the fuzzy weight of the brain prefrontal lobe system through a self-adaptive learning rule and a supervised learning mode;
a calculating module 400, configured to obtain a defuzzification operator of the amygdala system according to the fuzzy weight of the amygdala system and the linear relationship of the fuzzy set, and obtain a defuzzification operator of the brain prefrontal system according to the fuzzy weight of the brain prefrontal system and the linear relationship of the fuzzy set;
the output module 500 is configured to obtain a defuzzification output result according to the defuzzification operator of the amygdala system and the defuzzification operator of the brain prefrontal lobe system, and obtain a simulation result of a brain emotion learning model according to the defuzzification output result and use the simulation result in the simulation of an actual object.
Preferably, the method further comprises the following steps: the setting module 600 is configured to preset an initial fuzzy weight, an initial learning rate, and an emotion signal adjustment parameter of the amygdala system, and an initial fuzzy weight, an initial learning rate, and an emotion signal adjustment parameter of the brain prefrontal lobe system.
The calculation module is also used for calculating the defuzzification operator of the almond kernel system according to the formula:
the defuzzification operator of the brain prefrontal lobe system has a calculation formula of
Wherein n isiIs the input dimension, siIs the ith output, vioIs the weight value of the kernel fuzzy set of the output of the oioThe weight value of the o-th output of the brain prefrontal leaf blur set.
The calculation formula for obtaining the output result according to the defuzzification operator of the amygdala system and the defuzzification operator of the brain prefrontal leaf system is as follows: y iso＝ao-po。
Third embodiment of the invention:
a third embodiment of the present invention provides a terminal device including a processor, a memory, and a computer program stored in the memory and configured to be executed by the process. When the processor executes the computer program, the steps in any one of the embodiments of the wavelet fuzzy brain emotion learning control method described above are implemented, for example, step S10 shown in fig. 2. Alternatively, the processor, when executing the computer program, implements the functions of the above examples of the apparatus, such as the obtaining module 100 shown in fig. 3.
The fourth embodiment of the present invention:
a fourth embodiment of the present invention provides a computer-readable storage medium including a stored computer program, such as a program of a wavelet fuzzy brain emotion learning control method. When the computer program runs, the device where the computer readable storage medium is located is controlled to execute the wavelet fuzzy brain emotion learning control method in the first embodiment.
Illustratively, the computer programs described in the third and fourth embodiments of the present invention may be partitioned into one or more modules, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules can be a series of instruction segments of a computer program capable of achieving specific functions, and the instruction segments are used for describing the implementation process of the computer program in the device for realizing the wavelet fuzzy brain emotion learning control. For example, the apparatus described in embodiment two of the present invention.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. The general processor can be a microprocessor or the processor can also be any conventional processor and the like, the processor is a control center of the wavelet fuzzy brain emotion learning control method, and various interfaces and circuits are used for connecting all parts of the whole wavelet fuzzy brain emotion learning control method.
The memory can be used for storing the computer program and/or the module, and the processor can realize various functions of the wavelet fuzzy brain emotion learning control method by running or executing the computer program and/or the module stored in the memory and calling the data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, a text conversion function, etc.), and the like; the storage data area may store data (such as audio data, text message data, etc.) created according to the use of the cellular phone, etc. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
Wherein the module implementing the identification and control device for a nonlinear system with uncertain characteristics, if implemented in the form of software functional units and sold or used as a stand-alone product, can be stored in a computer readable storage medium. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
It should be noted that the above-described device embodiments are merely illustrative, where the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. In addition, in the drawings of the embodiment of the apparatus provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, and may be specifically implemented as one or more communication buses or signal lines. One of ordinary skill in the art can understand and implement it without inventive effort.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.
Claims (10)
1. A wavelet fuzzy brain emotion learning control method is characterized by comprising the following steps:
acquiring an input variable;
mapping the input variable through a wavelet function to obtain a fuzzy set;
simulating the learning process of the feeling and emotion of the brain according to the fuzzy rule of the pre-established wavelet fuzzy brain emotion learning controller, and updating the fuzzy weight of an amygdala system and the fuzzy weight of a brain prefrontal lobe system through a self-adaptive learning rule and a supervised learning mode;
obtaining a defuzzification operator of the amygdala system according to the fuzzy weight of the amygdala system and the linear relation of the fuzzy set, and obtaining a defuzzification operator of the brain prefrontal leaf system according to the fuzzy weight of the brain prefrontal leaf system and the linear relation of the fuzzy set;
and obtaining a defuzzification output result according to the defuzzification operator of the amygdala system and the defuzzification operator of the brain prefrontal leaf system, and obtaining a simulation result of the brain emotion learning control model according to the defuzzification output result and using the simulation result for simulating actual things.
2. The wavelet fuzzy brain emotion learning control method of claim 1, wherein the fuzzy rule of the pre-established wavelet fuzzy brain emotion learning controller is as follows: amygdala system:
then ao＝vio，for i＝1,2,...,ni,o＝1,2,...,noand the prefrontal system of the brain
then po＝wio，for i＝1,2,...,ni,o＝1,2,...,no(ii) a Before the step of simulating the learning process of the feeling and emotion of the brain according to the fuzzy rule of the pre-established wavelet fuzzy brain emotion learning controller and updating the fuzzy weight of the amygdala system and the fuzzy weight of the brain prefrontal lobe system through the self-adaptive learning rule and the supervised learning mode, the method comprises the following steps of: presetting initial fuzzy weight, initial learning rate and emotion signal adjusting parameters of an amygdala system and initial fuzzy weight, initial learning rate and emotion signal adjusting parameters of a brain prefrontal lobe system; wherein n isiIs the input dimension, noIs the total dimension of the output, IiIs the ith input variable; siIs the ith output, vioIs the weight value of the kernel fuzzy set of the output of the oioWeight value of the fuzzy set of prefrontal brain leaves, a, of the o-th outputoFor the output of the kernel fuzzy set of the No. o, poIs the output of the o-th fuzzy set of brain prefrontal lobes.
3. The wavelet fuzzy brain emotion learning control method of claim 1, wherein the defuzzification operator of the amygdala system has the calculation formula:
the defuzzification operator of the brain prefrontal lobe system has a calculation formula of
Wherein n isiIs the input dimension, siIs the ith output, vioIs the weight value of the kernel fuzzy set of the output of the oioThe weight value of the o-th output of the brain prefrontal leaf blur set.
4. The wavelet fuzzy brain emotion learning control method according to claim 1, wherein a calculation formula for obtaining the output result according to a defuzzification operator of the amygdala system and a defuzzification operator of the brain prefrontal lobe system is as follows: y iso＝ao-po。
5. A wavelet fuzzy brain emotion learning control device is characterized by comprising:
the acquisition module is used for acquiring input variables; the mapping module is used for mapping the input variable through a wavelet function to obtain a fuzzy set;
the updating module is used for simulating the learning process of the feeling and emotion of the brain according to the fuzzy rule of the wavelet fuzzy brain emotion learning controller established in advance, and updating the fuzzy weight of the amygdala system and the fuzzy weight of the brain prefrontal lobe system through a self-adaptive learning rule and a supervised learning mode;
the calculation module is used for obtaining a defuzzification operator of the amygdala system according to the fuzzy weight of the amygdala system and the linear relation of the fuzzy set and obtaining a defuzzification operator of the brain prefrontal leaf system according to the fuzzy weight of the brain prefrontal leaf system and the linear relation of the fuzzy set;
and the output module is used for obtaining a defuzzification output result according to the defuzzification operator of the amygdala system and the defuzzification operator of the brain prefrontal leaf system, and obtaining a simulation result of a brain emotion learning model according to the defuzzification output result and simulating actual objects.
6. The wavelet fuzzy brain emotion learning control device of claim 5, comprising:
and the setting module is used for presetting the initial fuzzy weight, the initial learning rate and the emotion signal adjusting parameter of the amygdala system and the initial fuzzy weight, the initial learning rate and the emotion signal adjusting parameter of the brain prefrontal lobe system.
7. The wavelet fuzzy brain emotion learning control device of claim 5, wherein the defuzzification operator of the amygdala system has the calculation formula:
the defuzzification operator of the brain prefrontal lobe system has a calculation formula of
Wherein n isiIs the input dimension, siIs the ith output, vioIs the weight value of the kernel fuzzy set of the output of the oioThe weight value of the o-th output of the brain prefrontal leaf blur set.
8. The wavelet fuzzy brain emotion learning control device of claim 5, wherein a calculation formula for obtaining the output result according to the defuzzification operator of the amygdala system and the defuzzification operator of the brain prefrontal lobe system is as follows: y iso＝ao-po。
9. A terminal device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements a wavelet-blurred brain emotion learning control method according to any one of claims 1 to 4 when executing the computer program.
10. A computer-readable storage medium, comprising a stored computer program, wherein when the computer program runs, the computer-readable storage medium controls a device to execute the wavelet fuzzy brain emotion learning control method according to any one of claims 1 to 4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810927199.0A CN108958037B (en) | 2018-08-15 | 2018-08-15 | Wavelet fuzzy brain emotion learning control method, device, equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810927199.0A CN108958037B (en) | 2018-08-15 | 2018-08-15 | Wavelet fuzzy brain emotion learning control method, device, equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108958037A CN108958037A (en) | 2018-12-07 |
| CN108958037B true CN108958037B (en) | 2021-06-15 |
Family
ID=64469046
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810927199.0A Active CN108958037B (en) | 2018-08-15 | 2018-08-15 | Wavelet fuzzy brain emotion learning control method, device, equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108958037B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111611747B (en) * | 2020-05-25 | 2022-09-09 | 福州大学 | Online state estimation method and device for hybrid energy storage system |
| CN111844047B (en) * | 2020-08-04 | 2021-06-11 | 河北省科学院应用数学研究所 | Dexterous hand control method and device and terminal equipment |
| CN111844049B (en) * | 2020-08-04 | 2021-08-17 | 河北省科学院应用数学研究所 | Dexterous hand grabbing control method and device and terminal equipment |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101782743A (en) * | 2010-02-11 | 2010-07-21 | 浙江大学 | Neural network modeling method and system |
| CN103197683A (en) * | 2013-04-18 | 2013-07-10 | 南京航空航天大学 | Method for intelligently controlling flight heights and attitudes of quad-rotor unmanned helicopters |
| CN104991446A (en) * | 2015-05-21 | 2015-10-21 | 南京航空航天大学 | Unmanned plane thrust direction-changing intelligent control method based on brain emotion learning |
| CN106650921A (en) * | 2016-09-28 | 2017-05-10 | 厦门理工学院 | Interval value type intuitive fuzzy cerebellum nerve network model |
| CN107392315A (en) * | 2017-07-07 | 2017-11-24 | 中南大学 | A kind of method for optimizing brain emotion learning model |
| CN107450321A (en) * | 2017-08-30 | 2017-12-08 | 北京石油化工学院 | A kind of Mechanical arm control method based on brain emotion learning |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140324747A1 (en) * | 2013-04-30 | 2014-10-30 | Raytheon Company | Artificial continuously recombinant neural fiber network |
-
2018
- 2018-08-15 CN CN201810927199.0A patent/CN108958037B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101782743A (en) * | 2010-02-11 | 2010-07-21 | 浙江大学 | Neural network modeling method and system |
| CN103197683A (en) * | 2013-04-18 | 2013-07-10 | 南京航空航天大学 | Method for intelligently controlling flight heights and attitudes of quad-rotor unmanned helicopters |
| CN104991446A (en) * | 2015-05-21 | 2015-10-21 | 南京航空航天大学 | Unmanned plane thrust direction-changing intelligent control method based on brain emotion learning |
| CN106650921A (en) * | 2016-09-28 | 2017-05-10 | 厦门理工学院 | Interval value type intuitive fuzzy cerebellum nerve network model |
| CN107392315A (en) * | 2017-07-07 | 2017-11-24 | 中南大学 | A kind of method for optimizing brain emotion learning model |
| CN107450321A (en) * | 2017-08-30 | 2017-12-08 | 北京石油化工学院 | A kind of Mechanical arm control method based on brain emotion learning |
Non-Patent Citations (2)
| Title |
|---|
| Integration of Fuzzy CMAC and BELC Networks for Uncertain Nonlinear System Control;Dajun Zhou;《2017 IEEE FUZZY》;20170712;1-6 * |
| 改进BELVC的四轮驱动机器人运动控制方法;王建彬;《华中科技大学学报（自然科学版）》;20131031;第41卷(第增刊Ⅰ期);9-12 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108958037A (en) | 2018-12-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108958037B (en) | Wavelet fuzzy brain emotion learning control method, device, equipment and storage medium | |
| Cao et al. | Support vector machine with adaptive parameters in financial time series forecasting | |
| CN108897829B (en) | Data label correction method, device and storage medium | |
| US20170039471A1 (en) | Neural network learning device | |
| CN108665175A (en) | A kind of processing method, device and the processing equipment of insurance business risk profile | |
| CN111507552A (en) | Deep learning method, system, server and readable storage medium | |
| CN110930996B (en) | Model training method, voice recognition method, device, storage medium and equipment | |
| CN112100928B (en) | Method and device for improving performance of lithium ion battery based on temperature | |
| CN112818588B (en) | Optimal power flow calculation method, device and storage medium of power system | |
| CN109102468B (en) | Image enhancement method and device, terminal equipment and storage medium | |
| CN112508299A (en) | Power load prediction method and device, terminal equipment and storage medium | |
| CN113987941A (en) | Time series prediction method, device, computer equipment and readable storage medium | |
| CN113705929B (en) | Spring festival holiday load prediction method based on load characteristic curve and typical characteristic value fusion | |
| CN111542818A (en) | Network model data access method and device and electronic equipment | |
| WO2021256135A1 (en) | Control device, method, and program | |
| CN114298319A (en) | Method and device for determining joint learning contribution value, electronic equipment and storage medium | |
| CN110111135B (en) | Power generation side member dynamic quotation decision method, device and equipment | |
| CN106899295A (en) | A kind of Randomized Quantizing signal decoding method and system | |
| CN112036461A (en) | Handwritten digital image recognition method, device, equipment and computer storage medium | |
| CN114492789B (en) | Neural network model construction method and device for data samples | |
| Chen et al. | Approximation of the piecewise function using neural fuzzy networks with an improved artificial bee colony algorithm | |
| CN110110853A (en) | A kind of deep neural network compression method, device and computer-readable medium | |
| CN110504709B (en) | Photovoltaic cluster reactive voltage regulation and control method, terminal equipment and storage medium | |
| Tang et al. | A Fast Kernel Least Mean Square Algorithm | |
| CN117650584A (en) | Simulation method and simulation device for power grid operation and power grid system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |