Wavelet fuzzy brain emotional learning control method and device, terminal device 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 incomplete and uncertain system information acquisition, ignoring human brain learning emotional factors, etc., to achieve good stability, rapid convergence, The effect of improving adaptive capacity

Active Publication Date: 2018-12-07
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, there is one-sidedness

Method used

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  • Wavelet fuzzy brain emotional learning control method and device, terminal device and storage medium
  • Wavelet fuzzy brain emotional learning control method and device, terminal device and storage medium
  • Wavelet fuzzy brain emotional learning control method and device, terminal device and storage medium

Examples

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no. 1 example

[0041] see figure 2 , figure 2It is a schematic flow chart of the wavelet fuzzy brain emotion learning control method provided by the first embodiment of the present invention. Embodiments of the present invention provide a wavelet fuzzy brain emotion learning control method, specifically comprising 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 controller mode...

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. Embodiments of the present invention provide a wavelet fuzzy brain emotion learning control device, specifically comprising:

[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 according to th...

no. 4 example

[0075] The fourth embodiment of the present invention provides a computer-readable storage medium, which includes a stored computer program, such as 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 the implementation of the wavelet fuzzy b...

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Abstract

The invention discloses a wavelet fuzzy brain emotional learning control method and device, a terminal device and a storage medium. The method comprises: obtaining an input variable; mapping the inputvariable by a wavelet function to obtain a fuzzy set; simulating a sensory and emotional learning process of a brain according to a pre-established fuzzy rule; updating the fuzzy weight of an amygdala system and the fuzzy weight of a prefrontal lobe system by an adaptive learning rule and a supervised learning method; obtaining the defuzzification operator of the amygdala system according to a linear relationship between the fuzzy weight of the amygdala system and the fuzzy set, and obtaining the defuzzification operator of the prefrontal lobe system according to a linear relationship betweenthe fuzzy weight of the prefrontal lobe system and the fuzzy set; obtaining an defuzzification output result according to the defuzzification operator of the amygdala system and the defuzzification operator of the prefrontal lobe system; and according to the defuzzification output result, obtaining the simulation result of the brain emotional learning control model for the control simulation of actual things.

Description

technical field [0001] The present 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 fuzziness 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 ...

Claims

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Application Information

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Patent Type & Authority Applications(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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