Encoding Nondeterministic Fuzzy Tree Automata Into Recursive Neural Networks  

Oleh:

Gori, M. ; Petrosino, A.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 15 no. 6 (Nov. 2004), page 1435-1449.
Topik: NEURAL NETWORKS; encoding; non deterministic; fuzzy tree; automata; recursive; neural networks

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.11 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

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 non deterministic 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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