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• W2068969130 abstract "In this paper finite automata are treated as general discrete dynamical systems from the viewpoint of systems theory. The unconditional on-line identification of an unknown finite automaton is the problem considered. A generalized architecture of recurrent neural networks with a corresponding on-line learning scheme is proposed as a solution to the problem. An on-line rule-extraction algorithm is further introduced. The architecture presented, the on-line learning scheme and the on-line rule-extraction method are tested on different, strongly connected automata, ranging from a very simple example with two states only to a more interesting and complex one with 64 states; the results of both training and extraction processes are very promising." @default.
• W2068969130 created "2016-06-24" @default.
• W2068969130 creator A5045910222 @default.
• W2068969130 creator A5070499887 @default.
• W2068969130 date "2003-01-01" @default.
• W2068969130 modified "2023-10-16" @default.
• W2068969130 title "On-line identification and reconstruction of finite automata with generalized recurrent neural networks" @default.
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• W2068969130 doi "https://doi.org/10.1016/s0893-6080(02)00221-6" @default.
• W2068969130 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/12576110" @default.
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