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Representation of finite state automata in recurrent radial basis function networks. (English) Zbl 0849.68082

Summary: We propose some techniques for injecting finite state automata into Recurrent Radial Basis Function networks (R\(^2\)BF). When providing proper hints and constraining the weight space properly, we show that these networks behave as automata. A technique is suggested for forcing the learning process to develop automata representations that is based on adding a proper penalty function to the ordinary cost. Successful experimental results are shown for inductive inference of regular grammars.

MSC:

68Q45 Formal languages and automata
68T05 Learning and adaptive systems in artificial intelligence
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