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Home > Archive>Volume 11, Issue 4, 2000 >447-452
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Error Analysis in Nonlinear System Identification Using Fuzzy System
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    Abstract:

    In this paper, the numerical approximation characteristics of fuzzy system are discussed, and the influence of approximation error and initial state error on fuzzy system are analyzed. Finally, an important conclusion is obtained that under some conditions, fuzzy system output differs little from that of the actual system.

    Reference
    1　 Lin C T. Neural Fuzzy Systems. New York: Prentice-Hall Press, 1997. 481～490 2　 Pao Xiaohong et al. Model error analysis in nonlinear system identification using neural networks (I). Control and Decision, 1997,12(5):20～25 3　 Wang Shi-tong et al. Fuzzy neural system and its application. Beijing: Publishing House of Beijing University of Aeronautics and Astronautics, 1996. 200～208 (王士同等.模糊神经系统及其应用.北京：北京航空航天大学出版社,1996.200～208) 4　 Freeman J. Learning and Generalization in RBFN. Journal of Neural Computation, 1995,7(3):32～36 5　 Niyogi D, Girosi F. On the relationship between generalization error, hypothesis complexity and sample complexity for RBFN. AI Laboratory, MIT, 1994. 103～105
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王士同,於东军.非线性系统的模糊辨识误差分析.软件学报,2000,11(4):447-452

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History
  • Received:July 06,1998
  • Revised:August 25,1998
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