Encoding nondeterministic fuzzy tree automata into recursive neural networks

• Published in: IEEE transactions on neural networks Vol. 15; no. 6; pp. 1435 - 1449
• Main Authors: Gori, M., Petrosino, A.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.11.2004
• Subjects: 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
• DOI: 10.1109/TNN.2004.837585

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.