Learning and Identifying Finite State Automata with Recurrent High-Order Neural Networks

Yasuaki Kuroe

doi:10.11499/sicep.2004.0_1_2

SICE Annual Conference 2004

DOI https://doi.org/10.11499/sicep.2004.0_1_2

会議情報

主催: The Society of Instrument and Control Engineers

共催: The IEEE Industrial Electronics Society, The IEEE Robotics and Automation Society, The IEEE Control Systems Society, The IEEE Systems, Man and Cybernetics Society, The Instrumentation, Systems, and Automation Society

Learning and Identifying Finite State Automata with Recurrent High-Order Neural Networks

*Yasuaki Kuroe

著者情報

会議録・要旨集フリー

p. 1

詳細

抄録

This paper proposes neural network models for learning and identifying deterministic finite state automata (FSA). The proposed models are a class of high-order recurrent neural networks. The models are capable of representing FSA with the network size being smaller than the existing models. We also discuss an identification method of FSA by training the proposed models of neural networks.

責任著者(Corresponding author)

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