Nonlinear System Control Using Compensatory Neuro-Fuzzy Networks(Optimization and Control)(<Special Section>Nonlinear Theory and its Applications)

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概要

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• In this paper, a Compensatory Neuro-Fuzzy Network (CNFN) for nonlinear system control is proposed. The compensatory fuzzy reasoning method is using adaptive fuzzy operations of neural fuzzy network that can make the fuzzy logic system more adaptive and effective. An on-line learning algorithm is proposed to automatically construct the CNFN. They are created and adapted as on-line learning proceeds via simultaneous structure and parameter learning. The structure learning is based on the fuzzy similarity measure and the parameter learning is based on backpropagation algorithm. The advantages of the proposed learning algorithm are that it converges quickly and the obtained fuzzy rules are more precise. The performance of CNFN compares excellently with other various exiting model.

• 社団法人電子情報通信学会の論文
• 2003-09-01

著者

• Lin Cheng-jian

  Department Of Computer Science And Information Engineering Chaoyang University Of Technology

• Chen Cheng-hung

  Department Of Computer Science And Information Engineering Chaoyang University Of Technology

関連論文

• Nonlinear System Control Using Compensatory Neuro-Fuzzy Networks(Optimization and Control)(Nonlinear Theory and its Applications)

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