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Nonlinear system identification method

A nonlinear system and identification method technology, applied in character and pattern recognition, instruments, artificial life, etc., can solve problems such as inapplicability of nonlinear system models

Active Publication Date: 2020-06-23
JIMEI UNIV
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Problems solved by technology

However, these two methods are not suitable for nonlinear system models

Method used

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Embodiment Construction

[0040] In order to further illustrate the embodiments, the present invention provides drawings. These drawings are a part of the disclosure of the present invention, which are mainly used to illustrate the embodiments, and can be combined with related descriptions in the specification to explain the operating principles of the embodiments. With reference to these contents, those skilled in the art should understand other possible implementations and advantages of the present invention.

[0041] The present invention will be further described in conjunction with the accompanying drawings and specific embodiments.

[0042] The embodiment of the present invention provides a nonlinear system identification method, such as figure 1 shown, including the following steps:

[0043] S1: Select multiple different nonlinear systems, collect multiple discrete samples corresponding to each nonlinear system, and compose all discrete samples of all nonlinear systems into a training set.

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Abstract

The invention relates to a nonlinear system identification method which comprises the following steps: S1, selecting a plurality of different nonlinear systems, collecting a plurality of discrete samples corresponding to each nonlinear system, and forming a training set by using all the discrete samples of all the nonlinear systems; s2, performing dimension raising processing on discrete samples in the training set; s3, constructing a width learning model, and performing optimization training on model parameters of the width learning model through the training set subjected to dimension raising processing and a particle swarm optimization algorithm to enable the value of an error function corresponding to the trained width learning model to be minimum; and S4, identifying the nonlinear system through the trained width learning model. According to the method, the nonlinear system is identified through a method of combining width learning and a particle swarm optimization algorithm, andthe method has obvious advantages in the aspects of prediction precision and interference resistance.

Description

technical field [0001] The invention relates to the field of system identification, in particular to a nonlinear system identification method. Background technique [0002] Nonlinear dynamic systems widely exist in fields such as communication, control, and mode identification. The purpose of system identification is to establish an approximation model of the controlled system, which can be used to analyze the performance, dynamic or static response characteristics of the system to improve the structure of the system. and parameters, so it has received extensive attention from many scholars. Dynamic systems Engineers face various problems in different application domains. For example, in the field of control, the most common problems encountered are system identification and control, which are not as simple as static systems. Researchers from different fields have developed several methods to build mathematical models for system identification. [0003] Since most systems...

Claims

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Application Information

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IPC IPC(8): G06N3/00G06K9/62
CPCG06N3/006G06F18/214Y02T10/40
Inventor 王荣杰韩冉曾广淼林安辉王亦春
Owner JIMEI UNIV
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