Abstract
The paper addresses the problem of online adaptive learning in a neuro-fuzzy network based on Sugeno-type fuzzy inference. A new learning algorithm for tuning of both antecedent and consequent parts of fuzzy rules is proposed. The algorithm is derived from the well-known Marquardt procedure and uses approximation of the Hessian matrix. A characteristic feature of the proposed algorithm is that it does not require time-consuming matrix operations. Simulation results illustrate application to adaptive identification of a nonlinear plant and nonlinear time series prediction.
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Bodyanskiy, Y., Kolodyazhniy, V., Stephan, A. (2001). An Adaptive Learning Algorithm for a Neuro-fuzzy Network. In: Reusch, B. (eds) Computational Intelligence. Theory and Applications. Fuzzy Days 2001. Lecture Notes in Computer Science, vol 2206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45493-4_11
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DOI: https://doi.org/10.1007/3-540-45493-4_11
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