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Fuzzy Model Identification Based on Rough Set Data Analysis

Part of the Control Engineering book series (CONTRENGIN)

Abstract

It is an open problem to model nonlinear systems with uncertainties. In Chapter 2, we developed an identification algorithm based on the Takagi-Sugeno fuzzy model. The fuzzy modeling procedure in Chapter 2 can be divided into three steps: premise structure identification, premise parameters identification, and consequent parameters identification. The premise structure identification procedure is done in two phases: (1) Identify the input structure, i.e., the significant input variables are identified among all possible input candidates; (2) assign fuzzy membership functions. In Chapter 2, we introduced an identification algorithm which included both phases in a uniform processes. We can also deal with them in two individual processes.

Keywords

Artificial Neural Network Artificial Neural Network Modeling Fuzzy Rule Fuzzy Model Fuzzy Membership Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© Birkhäuser Boston 2006

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