@inproceedings{fce48160e9064652a82c1e2d03aad47b,

title = "Fuzzy knowledge and recurrent neural networks: A dynamical systems perspective",

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 appli- cations can be modeled in the form of finite-state automata. This chap- ter 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 pa- rameters 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.",

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

year = "2000",

month = jan,

day = "1",

language = "English (US)",

isbn = "3540673059",

series = "Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)",

publisher = "Springer Verlag",

pages = "123--143",

editor = "Ron Sun and Stefan Wermter",

booktitle = "Hybrid Neural Systems",

address = "Germany",

note = "International Workshop on Hybrid Neural Systems, 1998 ; Conference date: 04-12-1998 Through 05-12-1998",

}