On-line identification of MIMO evolving Takagi-Sugeno fuzzy models

Angelov, Plamen and Xydeas, C and Filev, D (2004) On-line identification of MIMO evolving Takagi-Sugeno fuzzy models. In: International Joint Conference on Neural Networks and Fuzzy Systems, IJCNN-FUZZ-IEEE, 2004-07-25 - 2004-07-29.

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Evolving Takagi-Sugeno (eTS) fuzzy models and the method for their on-line identification has been recently introduced as an effective tool for design of flexible system models with minimum a priori information. Their structure develops on-line during the process of model identification itself. In this paper, this approach has been extended for the case of multi-input multi-output (MIMO) system model. Both parts of the identification algorithm, namely the unsupervised fuzzy rule-base antecedents learning by a recursive, noniterative clustering, and the supervised linear sub-model parameters learning by Kalman-filtering-based procedure, are extended for the MIMO case. The radius of influence of each fuzzy rule is considered a vector instead of a scalar as in the original eTS approach, allowing different areas of the data space to be covered by each input variable. As in the eTS, in MIMO eTS, the rule-base and parameters of the fuzzy model continually evolve by adding new rules with more summarization power and by modifying existing rules and parameters. Simulation results using a well-known benchmark are considered in this paper. Further investigation concern the application of MIMO eTS to predictive modeling of the speech spectrum magnitude, classification of multi-channel source modulation etc. (c) IEEE Press

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International Joint Conference on Neural Networks and Fuzzy Systems, IJCNN-FUZZ-IEEE
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?? takagi-sugeno fuzzy models flexible system models multiinput multioutput system online identification rule-base system unsupervised fuzzy rule-base antecedents learningdcs-publications-idinproc-341dcs-publications-creditsdsp-fadcs-publications-personnel-i ??
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11 Jan 2008 11:26
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24 Apr 2024 23:42