Representation of fuzzy finite state automata in continuous recurrent, neural networks

Based on previous work on encoding deterministic finite-state automata (DFA) in discrete-time, second-order recurrent neural networks with sigmoidal discriminant functions, we propose an algorithm that constructs an augmented recurrent neural network that encodes fuzzy finite-state automata (FFA). Given an arbitrary FFA, we apply an algorithm which transforms the FFA into an equivalent deterministic acceptor which computes the fuzzy string membership function. The neural network can be constructed such that it recognizes strings of fuzzy regular languages with arbitrary accuracy.

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