Nonlinear control method of neural network based on imitated operant conditioned reflex

A non-linear control and conditioned reflex technology, applied in the directions of adaptive control, general control system, control/regulation system, etc., can solve the problems of short time and imperfect theoretical system of type III controller, and reduce manual parameter adjustment work. , improve the effectiveness, the effect of a wide range of system operating conditions

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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Problems solved by technology

However, the type III controller has appeared for a relatively short time, and its theoretica

Method used

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  • Nonlinear control method of neural network based on imitated operant conditioned reflex
  • Nonlinear control method of neural network based on imitated operant conditioned reflex
  • Nonlinear control method of neural network based on imitated operant conditioned reflex

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Embodiment Construction

[0050] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0051] The present embodiment imitates the neural network nonlinear control method of operant conditioning, and comprises the following steps:

[0052] Step 1. Establish an operant conditioning bionic model:

[0053] In the nervous system of higher organisms, neuron inclusions gather in the central nervous system to form neural nuclei, and neuron nuclei with similar functions gather to form neural nuclei. Inspired by this principle, the present invention classifies neurons in the network according to different neural activities, so that neurons with the same neural activity form a neural adaptive unit, and the overall network is composed of M neural units, then the i-th Neural activity of neurons:

[0054]

[0055] Among them, μ i ∈ R q , σ i ∈R is the parameter of the i-th neuron cluster, z=[z 1 ,z 2 ,...,z q ] T input to the neural network...

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Abstract

The invention discloses a nonlinear control method of a neural network based on imitated operant conditioned reflex. The method comprises: step one, an operational conditional reflex bionic model is established; step two, a class of non-affine system based on the operational conditional reflex bionic model is established; step three, a controller u is designed by using an OCBM bionic network; and step four, the controller u is applied to the non-affine system, so that an output y(t) tracks an expected track xd (t) based on a given precision beta0 and a system tracking error e(t) is ensured to have a boundary in a range of being larger than or equal to 0 by t. According to the invention, on the basis of the biological principle of the operational conditional reflection, a bionic-inspired artificial neural network is constructed and the network is applied to deal with an unknown nonlinear term of a complicated uncertain system; and a control strategy based on an operational conditional reflection model is designed for one class of uncertain non-affine system, so that the effectiveness, flexibility and adaptability of neural network control are improved.

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] Artificial Neural Network (ANN) is often used as a popular mathematical tool in the field of nonlinear system control because of its ability to approximate nonlinear functions on arbitrary norms. Among many achievements, neural network controllers can be divided into three types: Type I controllers for off-line training weights, Type II controllers for online weight learning based on fixed network structures, and online weight learning with adjustable network structures Type III controller. [0003] Up to now, the vast majority of research work has focused on the design and development of Type I and Type II controllers. However, the Type I controller lacks the adaptive ability of the offline weight training network, and the Type II con...

Claims

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Application Information

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