Bionic intelligent control method based on multi-connotation self-adjusting neural network

A neural network, intelligent control technology, applied in the direction of adaptive control, general control system, control/regulation system, etc., can solve problems affecting the effectiveness of NN control methods, etc.

Inactive Publication Date: 2017-10-20
青岛格莱瑞智能控制技术有限公司
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  • Application Information

AI Technical Summary

Problems solved by technology

These issues are not only challenging but also directly affect the effectiveness of NN control methods, so they deserve further attention and discussion

Method used

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  • Bionic intelligent control method based on multi-connotation self-adjusting neural network
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  • Bionic intelligent control method based on multi-connotation self-adjusting neural network

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Embodiment Construction

[0072] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0073] This embodiment is based on the bionic intelligent control method of multi-content self-regulating neural network, comprising the following steps:

[0074] Step 1. Design countermeasures against the application constraints of the universal approximation theorem, which includes:

[0075] (1) Construct the time-varying ideal synaptic connection weight, and the NN with time-varying ideal weight is expressed as

[0076]

[0077] Among them, the unknown time-varying ideal weight

[0078]

[0079] (2) Design an online self-regulation method for the number of neurons, the process is as follows figure 1 As shown, it specifically includes the following steps:

[0080] a. At t=t 0 = 0, the number of neurons in the initialization system is M(t i ) = m 0 , i=0; in order to prevent the asymptotic function failure of NN caused by too few neurons, m...

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Abstract

The invention discloses a bionic intelligent control method based on a multi-connotation self-adjusting neural network. The method comprises: step one, carrying out application restriction design countermeasure for a universal approximation theorem; step two, on the basis of the method, designing a multi-connotation self-adjusting neural network; step three, establishing a high-order non-affine system; step four, designing an MSAE-NN-based controller; and step five, applying a controller u to the non-affine system established at the step two to enable a system state x1 to track an expected track xd (t) precisely with modeling uncertainty and external interference existence. Therefore, a multi-connotation self-adjusting neural network having a time-varying ideal weight, a smoothing self-increasing neuron and a diversified basis function is constructed based on the working principle of the neural network and the multi-connotation self-adjusting neural network is applied to control of the uncertain high-order non-affine system, so that problems that are common and are ignored of the NN controller designed based on the universal approximation theorem are solved.

Description

technical field [0001] The invention relates to the fields of nonlinear system control and neural network control, in particular to a method for dealing with unknown nonlinear items of complex uncertain systems. Background technique [0002] The feature of NN's ability to learn arbitrary nonlinear functions was fully demonstrated in the 1990s. Compared with classical control and modern control theory, NN control method theoretically does not require complex mathematical analysis process and any prior knowledge, and is widely used in the control of uncertain nonlinear dynamic systems. Combined with adaptive control technology, adaptive NN control theory and nonlinear system stability analysis method based on Lyapunov method were subsequently developed. As we all know, the NN universal approximation property is based on some preconditions given by the universal approximation theorem (UAT). For any unknown function g(z), it can be reconstructed by the following formula [00...

Claims

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Application Information

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IPC IPC(8): G05B13/04
CPCG05B13/042
Inventor 宋永端贾梓筠张东赖俊峰
Owner 青岛格莱瑞智能控制技术有限公司
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