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Proceedings Paper

Fuzzy neural networks based on rough sets for process modeling
Author(s): Jianming Zhang
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Paper Abstract

A new constructive method of the fuzzy neural network based on rough sets is proposed. First, we generate an initial fuzzy rule base from the history input-output data pairs. Then, in order to obtain the optimal rule base, the inconsistent or redundant rules of the initial fuzzy system are reduced by means of the rough set theory. Finally, we implement an optimal and simple fuzzy neural network by mapping from the optimal rules and train it with the initial data pairs. Since we determine the proper network structure and initial weights in advance, we can train the fuzzy neural network rapidly. The application to modeling of a nonlinear process reveals that it is effective and has good performances. The merit of this new method is to optimize the overall structure of fuzzy neural networks as well as to adjust each parameter of fuzzy rules without doing the complicated clustering process.

Paper Details

Date Published: 2 September 2003
PDF: 4 pages
Proc. SPIE 5253, Fifth International Symposium on Instrumentation and Control Technology, (2 September 2003); doi: 10.1117/12.522151
Show Author Affiliations
Jianming Zhang, Zhejiang Univ. (China)

Published in SPIE Proceedings Vol. 5253:
Fifth International Symposium on Instrumentation and Control Technology
Guangjun Zhang; Huijie Zhao; Zhongyu Wang, Editor(s)

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