Rule Extraction from Recurrent Neural Networks: A Taxonomy and Review
Neural Computation
Spatiotemporal Connectionist Networks: A Taxonomy and Review
Neural Computation
Minimization of states in automata theory based on finite lattice-ordered monoids
Information Sciences: an International Journal
A self-organizing feature map-driven approach to fuzzy approximate reasoning
Expert Systems with Applications: An International Journal
Extracting symbolic knowledge from recurrent neural networks---A fuzzy logic approach
Fuzzy Sets and Systems
State fusion of fuzzy automata with application on target tracking
Computers & Mathematics with Applications
Fuzzy automata with ε-moves compute fuzzy measures between strings
Fuzzy Sets and Systems
New directions in fuzzy automata
International Journal of Approximate Reasoning
Theory research on a new type fuzzy automaton
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
Bridging control and artificial intelligence theories for diagnosis: A survey
Engineering Applications of Artificial Intelligence
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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 automats (FFAs) can model dynamical processes whose current state depends on the current input and previous states. Unlike in the case of deterministic finite-state automats (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 examine how the networks' performance varies as a function of synaptic weight strengths