Abstract
Hybrid neuro-fuzzy systems – the combination of artificial neural networks with fuzzy logic – are becoming increasingly popular. However, neuro-fuzzy systems need to be extended for applications which require context (e.g., speech, handwriting, control). Some of these applications can be modeled in the form of finite-state automata. This chapter presents a synthesis method for mapping fuzzy finite-state automata (FFAs) into recurrent neural networks. The synthesis method requires FFAs to undergo a transformation prior to being mapped into recurrent networks. Their neurons have a slightly enriched functionality in order to accommodate a fuzzy representation of FFA states. This allows fuzzy parameters of FFAs to be directly represented as parameters of the neural network. We present a proof the stability of fuzzy finite-state dynamics of constructed neural networks and through simulations give empirical validation of the proofs.
This chapter contains material reprinted from IEEE Transactions on Fuzzy Systems, Vol. 6, No. 1, p. 76-89, ©1998, Institute of Electrical and Electronics Engineers, and from Proceedings of the IEEE, Vol. 87, No. 9, p. 1623-1640, ©1999, Institute of Electrical and Electronics Engineers, by permission.
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Omlin, C.W., Giles, L., Thornber, K.K. (2000). Fuzzy Knowledge and Recurrent Neural Networks: A Dynamical Systems Perspective. In: Wermter, S., Sun, R. (eds) Hybrid Neural Systems. Hybrid Neural Systems 1998. Lecture Notes in Computer Science(), vol 1778. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10719871_9
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DOI: https://doi.org/10.1007/10719871_9
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