A new learning algorithm for a forecasting neuro-fuzzy network

Authors:
Peter Otto;Yevgeniy Bodyanskiy;Vitaliy Kolodyazhniy
Affiliations:
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: peter.otto@systemtechnik.tu-i ...;Control Systems Research Laboratory, Kharkiv National University of Radioelectronics, Lenin Av., 14, Kharkiv, 61166, Ukraine. Tel.: +38 0572 7021890/ E-mail: {bodyanskiy,kolodyazhniy}@ieee.org;Control Systems Research Laboratory, Kharkiv National University of Radioelectronics, Lenin Av., 14, Kharkiv, 61166, Ukraine. Tel.: +38 0572 7021890/ E-mail: {bodyanskiy,kolodyazhniy}@ieee.org
Venue:
Integrated Computer-Aided Engineering
Year:
2003

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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.