Encoding nondeterministic fuzzy tree automata into recursive neural networks

Fuzzy neural systems have been a subject of great interest in the last few years, due to their abilities to facilitate the exchange of information between symbolic and subsymbolic domains. However, the models in the literature are not able to deal with structured organization of information, that is...

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Vydáno v:IEEE transactions on neural networks Ročník 15; číslo 6; s. 1435 - 1449
Hlavní autoři: Gori, M., Petrosino, A.
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.11.2004
Témata:
Algorithms
Application software
Artificial Intelligence
Computer Simulation
Councils
Decision Support Techniques
Encoding
Feedback
Fuzzy Logic
Fuzzy neural networks
Fuzzy sets
Fuzzy systems
fuzzy tree automata
knowledge representation
Learning automata
Logistic Models
Neural networks
Neural Networks (Computer)
Pattern recognition
Pattern Recognition, Automated - methods
Production
recursive neural networks
ISSN:1045-9227
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Shrnutí:Fuzzy neural systems have been a subject of great interest in the last few years, due to their abilities to facilitate the exchange of information between symbolic and subsymbolic domains. However, the models in the literature are not able to deal with structured organization of information, that is typically required by symbolic processing. In many application domains, the patterns are not only structured, but a fuzziness degree is attached to each subsymbolic pattern primitive. The purpose of this paper is to show how recursive neural networks, properly conceived for dealing with structured information, can represent nondeterministic fuzzy frontier-to-root tree automata. Whereas available prior knowledge expressed in terms of fuzzy state transition rules are injected into a recursive network, unknown rules are supposed to be filled in by data-driven learning. We also prove the stability of the encoding algorithm, extending previous results on the injection of fuzzy finite-state dynamics in high-order recurrent networks.
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ISSN:1045-9227
DOI:10.1109/TNN.2004.837585