A Nonlinear System Identification Method Based on Fuzzy dynamical Model and State-Space Neural Network

被引：2
|
作者：
Huang, Xiaobin [1 ]
Qi, Hongjing [2 ]
机构：
[1] North China Elect Power Univ, Dept Automat, Beijing, Peoples R China
[2] North China Elect Power Univ, Dept Sci & Technol, Beijing, Peoples R China
来源：
2008 CHINESE CONTROL AND DECISION CONFERENCE, VOLS 1-11 | 2008年
关键词：
System Identification; Neural Networks; Fuzzy Logic; Nonlinear Systems;
D O I：
10.1109/CCDC.2008.4598229
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
A novel method of fuzzy modelling using multiple local state space neural networks is propesed to handle complex nonlinear dynamics. It combines fuzzy logic and neural networks by a sound framework. The overall nonlinear system is represented by a set of state-space neural networks, connected by fuzzy variables. The resulting neural networks can be directly represented as state-space format so that control and fault diagnosis based on state space equation becomes more straight and easier. The efficiency of this method is tested by applying to a typical nonlinear system: three water tank system.
引用
收藏
页码：4738 / +
页数：2
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