Affiliations: [a] Department of Informatics and Automation, Technical University of Ilmenau, PF 100565, 98684 Ilmenau, Germany. Tel.: +49 3677 69 27 73; Fax: +49 3677 69 14 34; E-mail: [email protected] | [b] Control Systems Research Laboratory, Kharkiv National University of Radioelectronics, Lenin Av., 14, Kharkiv, 61166, Ukraine. Tel.: +38 0572 7021890; E-mail: [email protected], [email protected]
Correspondence:
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Corresponding author.
Note: [1] Present address: Department of Informatics and Automation, Technical University of Ilmenau, 98684 Ilmenau, Germany. Tel.: +49 3677 69 1417; E-mail: [email protected].
Abstract: The article addresses the problem of adaptive learning in a neuro-fuzzy network based on Sugeno-type fuzzy inference. A new learning algorithm for tuning of both the antecedent and consequent parts of the fuzzy rules is proposed. The algorithm is derived from the Hartley and Marquardt methods. A characteristic feature of the proposed algorithm is that it does not include time-consuming matrix inversion operations. Simulation results prove the high performance of the algorithm and illustrate its application to time series forecasting.
