## Fuzzy Finite-state Automata Can Be Deterministically Encoded into Recurrent Neural Networks (1996)

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Citations: | 13 - 5 self |

### BibTeX

@MISC{Omlin96fuzzyfinite-state,

author = {Christian W. Omlin and Karvel K. Thornber and C. Lee Giles},

title = {Fuzzy Finite-state Automata Can Be Deterministically Encoded into Recurrent Neural Networks},

year = {1996}

}

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### Abstract

There has been an increased interest in combining fuzzy systems with neural networks because fuzzy neural systems merge the advantages of both paradigms. On the one hand, parameters in fuzzy systems have clear physical meanings and rule-based and linguistic information can be incorporated into adaptive fuzzy systems in a systematic way. On the other hand, there exist powerful algorithms for training various neural network models. However, most of the proposed combined architectures are only able to process static input-output relationships, i.e. they are not able to process temporal input sequences of arbitrary length. Fuzzy finite-state automata (FFAs) can model dynamical processes whose current state depends on the current input and previous states. Unlike in the case of deterministic finite-state automata (DFAs), FFAs are not in one particular state, rather each state is occupied to some degree defined by a membership function. Based on previous work on encoding DFAs in discrete-tim...