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Fuzzy Finite-state Automata Can Be Deterministically Encoded
into Recurrent Neural Networks
Christian W. Omlin(a), Karvel K. Thornber(a), C. Lee Giles(a,b)
(a)NEC Research Institute, Princeton, NJ 08540
(b)UMIACS, U. of Maryland, College Park, MD 20742
U. of Maryland Technical Report CS-TR-3599 and UMIACS-96-12
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-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 measuring string recognition performance of recurrent neural networks
which encode large randomly generated FFAs. In particular, we examine how
the networks' performance varies as a function of synaptic weight strength.
Keywords: Fuzzy logic, automata, fuzzy automata, recurrent neural networks,
encoding, rules.
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UMD-CS-TR-3599.fuzzy.automata.encoding.recurrent.nets.ps.Z
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