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一種基于模糊神經(jīng)網(wǎng)絡(luò)的非線性系統(tǒng)模型辨識方法

李映,  白本督,  焦李成

李映, 白本督, 焦李成. 一種基于模糊神經(jīng)網(wǎng)絡(luò)的非線性系統(tǒng)模型辨識方法[J]. 電子與信息學報, 2002, 24(3): 355-360.
引用本文: 李映, 白本督, 焦李成. 一種基于模糊神經(jīng)網(wǎng)絡(luò)的非線性系統(tǒng)模型辨識方法[J]. 電子與信息學報, 2002, 24(3): 355-360.
Li Ying, Bai Bendu, Jiao Licheng. A model identification approach of nonlinear-systems based on fuzzy neural networks[J]. Journal of Electronics & Information Technology, 2002, 24(3): 355-360.
Citation: Li Ying, Bai Bendu, Jiao Licheng. A model identification approach of nonlinear-systems based on fuzzy neural networks[J]. Journal of Electronics & Information Technology, 2002, 24(3): 355-360.

一種基于模糊神經(jīng)網(wǎng)絡(luò)的非線性系統(tǒng)模型辨識方法

A model identification approach of nonlinear-systems based on fuzzy neural networks

  • 摘要: 該文提出一種非線性系統(tǒng)的模型辨識方法。通過結(jié)構(gòu)的辨識(學習)和參數(shù)的辨識(學習),構(gòu)造了一個模糊神經(jīng)網(wǎng)絡(luò),經(jīng)調(diào)整網(wǎng)絡(luò)的權(quán)值,獲得一個精確的模糊模型。對兩個非線性系統(tǒng)辨識的仿真結(jié)果驗證了該方法的有效性。
    Abstract: This paper presents a model identification approach of nonlinear-systems. Through structure identification (learning) and parameter identification (learning), a fuzzy neural network is constructed. Having adjusted the weights of the fuzzy neural network, a precise fuzzy model is obtained. Finally, the effectiveness of the proposed technique is confirmed by the simulation results of two nonlinear systems.
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  • J.Nie,Constructing fuzzy model by self-organizing counterpropagation network,IEEE Trans.onsystem,Man and Cybernetics 1995,25(6),963-970.[2]R.P.Li,M.Mukaidono,Fuzzy modeling and clustering neural network.Control and Cybernetics.1996,25(2),225-242.[3]J.Jang,C.Sun,Neuro-fuzzy modeling and control.Proceeding of IEEE,1995,83(3),378-400.[4]J.Keller,H.Tahani,Neural nctwork implementation of fuzzy logic.Fuzzy Sets and Systems.1992,45(1): 1-12.[5]Y.C.Chen,C.C.Teng,A model reference control structure using a fuzzy neural network,Fuzzy Sets and Systems,1995,73(1),291-312.[6]C.C.Wong,C.C.Chen,A hybrid clustering and gradient descent approach for fiuzzy modeling,IEEE Trans.on system,Man and Cybernetics-Part B,1999,29(6),686-693.[7]L.X.Wang,M Mendel,Fuzzy basis function,universal alpproximation,and orthogonal leastsquares learning,IEEE Trans.on Neural Network,1992,3(5),807-814.[8]J.Castro,M.Delgado,Fuzzy systems with defuzzification are universal approximators,IEEE Traits.on system,Man and Cybernetics,1996,26(1): 149 152.[9]B.Kosko,Fuzzy systems as universal approximators,Proceeding of IEEE International Conference on Fuzzy System,San Diego,CA,1992,1153-1162.[10]F.Mauricio,G.Fernando,Design of fuzzy system using neurofuzzy networks,IEEE Trants.oon Neural Network,1999,10(4),815-827.
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出版歷程
  • 收稿日期:  2000-01-12
  • 修回日期:  2000-11-16
  • 刊出日期:  2002-03-19

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