paper on fuzzy automata and recurrent neural networks

[Lee Giles]

The following manuscript has been accepted in IEEE Transactions on
Fuzzy Systems and is available at the WWW site listed below:

www.neci.nj.nec.com/homepages/giles/papers/IEEE.TFS.fuzzy.automata.encoding.recurrent.net.ps.Z

We apologize in advance for any multiple postings that may be received.

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         Fuzzy Finite-State Automata Can Be Deterministically 
		Encoded Into Recurrent Neural Networks

     Christian W. Omlin(1), Karvel K. Thornber(2), C. Lee~Giles(2,3)

	(1)Adaptive Computing Technologies, Troy, NY 12180
	(2)NEC Research Institute, Princeton, NJ 08540
	(3)UMIACS, U. of Maryland, College Park, MD 20742

				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; 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-time, second-order
recurrent neural networks, we propose an algorithm that constructs an
augmented recurrent neural network that encodes a FFA and recognizes a
given fuzzy regular language with arbitrary accuracy. We then
empirically verify the encoding methodology by correct string
recognition of randomly generated FFAs. In particular, we examined how
the networks' performance varies as a function of synaptic weight
strengths

Keywords: Fuzzy systems, fuzzy neural networks, recurrent neural
networks, knowledge representation, automata, languages, nonlinear
systems.

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C. Lee Giles / Computer Science / NEC Research Institute / 
4 Independence Way / Princeton, NJ 08540, USA / 609-951-2642 / Fax 2482
www.neci.nj.nec.com/homepages/giles.html
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