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A nonlinear system identification method based on fuzzy dynamical model and state-space neural network

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2 Author(s)
Xiaobin Huang ; Department of Automation, North China Electric Power Universy, Beijing, China ; Hongjing Qi

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.

Published in:

2008 Chinese Control and Decision Conference

Date of Conference:

2-4 July 2008