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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, 2004-07-252004-07-29, Budapest, Hungary.

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    Abstract

    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

    Item Type: Conference or Workshop Item (Paper)
    Journal or Publication Title: International Joint Conference on Neural Networks and Fuzzy Systems, IJCNN-FUZZ-IEEE
    Additional Information: "©2004 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE." "This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder."
    Uncontrolled Keywords: Takagi-Sugeno fuzzy models flexible system models multiinput multioutput system online identification rule-base system unsupervised fuzzy rule-base antecedents learning ; DCS-publications-id ; inproc-341 ; DCS-publications-credits ; dsp-fa ; DCS-publications-personnel-id ; 82 ; 24
    Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
    Departments: Faculty of Science and Technology > School of Computing & Communications
    ID Code: 951
    Deposited By: Dr. Plamen Angelov
    Deposited On: 11 Jan 2008 11:26
    Refereed?: Yes
    Published?: Published
    Last Modified: 21 Mar 2014 10:27
    Identification Number:
    URI: http://eprints.lancs.ac.uk/id/eprint/951

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