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Wavelet fuzzy brain emotion learning control method, device, equipment and storage medium

A learning control and fuzzy technology, applied in the field of identification and control of nonlinear systems, can solve the problems of ignoring the emotional factors of human brain learning, incompleteness, limited information acquisition of uncertain systems, etc., and achieve rapid convergence, good stability, The effect of improving adaptive ability

Active Publication Date: 2021-06-15
XIAMEN UNIV OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the fuzzy language description of uncertain problems is limited, the acquisition of uncertain system information is usually limited and incomplete, the convergence and accuracy need to be further improved, and the existing neural network model ignores the emotion of human brain learning factor, one-sidedness

Method used

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  • Wavelet fuzzy brain emotion learning control method, device, equipment and storage medium
  • Wavelet fuzzy brain emotion learning control method, device, equipment and storage medium
  • Wavelet fuzzy brain emotion learning control method, device, equipment and storage medium

Examples

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no. 1 example

[0041] see figure 2 , figure 2 It is a schematic flowchart of the wavelet fuzzy brain emotion learning control method provided by the first embodiment of the present invention. An embodiment of the present invention provides a wavelet fuzzy brain emotion learning control method, which specifically includes the following steps:

[0042] S10, acquiring input variables.

[0043] Get the input variables of the amygdala fuzzy system and the prefrontal cortex fuzzy system Each input variable Ii corresponds to an external input signal input to the model, and also corresponds to the language variable of the fuzzy rules inside the mathematical model.

[0044]S20, map the input variable by wavelet function to obtain a fuzzy set

[0045] The wavelet function is used as the basis function to map the input variables to the fuzzy set of the brain emotional learning controller through the memory space: The fuzzy set is used inside the wavelet fuzzy brain-emotional learning controlle...

no. 2 example

[0063] see image 3 , image 3 It is a schematic structural diagram of the wavelet fuzzy brain emotion learning control device provided by the second embodiment of the present invention. An embodiment of the present invention provides a wavelet fuzzy brain emotion learning control device, which specifically includes:

[0064] An acquisition module 100, configured to acquire input variables.

[0065] A mapping module 200, configured to map the input variable through a wavelet function to obtain a fuzzy set;

[0066] The update module 300 simulates the learning process of the brain's sensation and emotion according to the fuzzy rules of the pre-established wavelet fuzzy brain emotion learning controller, and updates the fuzzy weights of the amygdala system and the frontal system of the brain through adaptive learning rules and supervised learning methods. fuzzy weight;

[0067] Calculation module 400, for obtaining the defuzzification operator of the amygdala system accordin...

no. 4 example

[0075] The fourth embodiment of the present invention provides a computer-readable storage medium, and the computer-readable storage medium includes a stored computer program, for example, a program of a wavelet fuzzy brain emotion learning control method. Wherein, when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the wavelet fuzzy brain emotion learning control method described in the first embodiment above.

[0076] Exemplarily, the computer program described in the third embodiment and the fourth embodiment of the present invention can be divided into one or more modules, the one or more modules are stored in the memory, and the The processor executes to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution of the computer program in t...

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Abstract

The invention discloses a wavelet fuzzy brain emotion learning control method, device, terminal equipment and storage medium. The method includes: acquiring input variables; mapping the input variables through wavelet functions to obtain fuzzy sets. According to the pre-established fuzzy rules, the learning process of the brain's feelings and emotions is simulated, and the fuzzy weights of the amygdala system and the prefrontal cortex system are updated through adaptive learning rules and supervised learning methods. The defuzzification operator of the amygdala system is obtained according to the fuzzy weight of the amygdala system and the linear relationship of the fuzzy set, and the defuzzification operator of the prefrontal system of the brain is obtained according to the fuzzy weight of the prefrontal system and the linear relationship of the fuzzy set. According to the defuzzification operator of the amygdala system and the defuzzification operator of the prefrontal system of the brain, the defuzzification output result is obtained, and according to the defuzzification output result, the simulation result of the brain emotional learning control model is obtained and used for real things control simulation.

Description

technical field [0001] The invention relates to the field of identification and control of nonlinear systems with uncertain characteristics, in particular to a wavelet fuzzy brain emotion learning control method, device, equipment and storage medium. Background technique [0002] In recent years, fuzzy systems and neural networks have been widely used in the identification and control of nonlinear systems. The fuzzy system can describe and deal with the ambiguity existing in human language and thinking, and the neural network can simulate the physiological structure and information processing process of the human brain. Imitating human intelligence is their common goal and basis for cooperation. However, the fuzzy language description of uncertain problems is limited, the acquisition of uncertain system information is usually limited and incomplete, the convergence and accuracy need to be further improved, and the existing neural network model ignores the emotion of human br...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G05B13/04
CPCG05B13/042
Inventor 赵晶林志民钟智雄徐敏
Owner XIAMEN UNIV OF TECH
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