PID (Proportional Integral Derivative) control method for elastic integral BP neural network based on RBF (Radial Basis Function) identification

A BP neural network and network technology, which is applied in the field of PID control system of elastic integral BP neural network, can solve the problems of difficult online real-time adjustment, weak robustness, and difficulty in adapting to changes in the external environment.

Inactive Publication Date: 2011-02-09
TIANJIN UNIVERSITY OF TECHNOLOGY
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Problems solved by technology

[0005] The purpose of the present invention is to solve the problem that the traditional PID control system is difficult to adjust in real time due to the fixed control parameters, and the rob

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  • PID (Proportional Integral Derivative) control method for elastic integral BP neural network based on RBF (Radial Basis Function) identification
  • PID (Proportional Integral Derivative) control method for elastic integral BP neural network based on RBF (Radial Basis Function) identification
  • PID (Proportional Integral Derivative) control method for elastic integral BP neural network based on RBF (Radial Basis Function) identification

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Embodiment 1

[0053] A PID control method based on elastic integral BP neural network identified by RBF (see figure 1 , figure 2 ), the method includes the following steps:

[0054] (1) Determine the structure of the BP network, and give the initial value of the weighting coefficient of each layer and Select learning rate η and inertia coefficient α, k=1;

[0055] (2) Determine the input nodes and the number m of the RBF identification network, the number s of hidden layer nodes, and give the center vector C of the hidden layer nodes j (0), the initial value b of the base broadband parameter j (0), the initial value of the weight coefficient w j (0), learning rate ρ, inertia coefficient γ, k=1;

[0056] (3) Sampling to get y(k), r(k), and calculate e(k);

[0057] (4) Forward calculation of the input and output of neurons in each layer of the BP network, the output of the BP output layer is the three adjustable parameters of the PID control; the deviation threshold ε is given, and...

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Abstract

The invention relates to a PID (Proportional Integral Derivative) control method for an elastic integral BP neural network based on RBF (Radial Basis Function) identification, which comprises the following steps: determining the structure of the BP neural network and determining an initial value; determining the structure of an RBF identification network; sampling; positively calculating the BP network and calculating the output of a PID control system; calculating the RBF identification network; revising the parameters of the identification network; and revising the weighting coefficient of the BP netural network. The invention has the advantages that the BP neural network is combined with the traditional PID control to form an intelligent neural network PID control system; no accurate mathematical model is required to be established; the change of the parameters of the controlled course, the parameters of the automatic setting control and the parameters of adapting to the controlled course can be automatically identified; and the method is an effective measure for solving the problems of difficult parameter setting, no real-time parameter adjustment and weak robustness of the traditional PID control system.

Description

【Technical field】: [0001] The invention belongs to the technical field of intelligent control, and relates to a PID control system based on improved parameter setting of BP neural network, in particular to a PID control system based on elastic integral BP neural network identified by RBF. 【Background technique】: [0002] The adjustment system controlled by proportional, integral and differential is called PID control system for short. It is the most widely used, oldest and most vigorous control method in industrial process control. In the current industrial production, more than 90% of the control systems It is a PID control system. It adopts the method based on the object mathematical model, which has the advantages of simple algorithm, good robustness, high reliability, and good control effect, so it is widely used in industrial control processes, especially for deterministic control systems that can establish accurate mathematical models. For the traditional PID control ...

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Application Information

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IPC IPC(8): G06N3/02G05B13/02
Inventor 马幼捷刘玥周雪松刘思佳刘进华于阳
Owner TIANJIN UNIVERSITY OF TECHNOLOGY
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