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

Conference information

Host: The Society of Instrument and Control Engineers

Co-host: 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

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Abstract

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.

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