Abstract
A Neuro-Fuzzy Network (NFN) is proposed, combining the merits of Artificial Neural Networks and Fuzzy Logic Systems. Most specifically, prior knowledge can be embedded in the synaptic weights of the NFN, speeding up the convergence.This NFN can be used for rule extraction or for identification and control of nonlinear dynamical systems.
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© 1993 Springer-Verlag
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Glorennec, P.Y., Barret, C., Brunet, M. (1993). Application of Neuro-Fuzzy Networks to the identification and control of nonlinear dynamical systems. In: Bouchon-Meunier, B., Valverde, L., Yager, R.R. (eds) IPMU '92—Advanced Methods in Artificial Intelligence. IPMU 1992. Lecture Notes in Computer Science, vol 682. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-56735-6_72
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DOI: https://doi.org/10.1007/3-540-56735-6_72
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