Stability of automata neural networks I

Ying Liu

doi:10.1117/12.144764

21 May 1993 Stability of automata neural networks I

Ying Liu

Proceedings Volume 1902, Nonlinear Image Processing IV; (1993) https://doi.org/10.1117/12.144764
Event: IS&T/SPIE's Symposium on Electronic Imaging: Science and Technology, 1993, San Jose, CA, United States

Abstract

In an earlier paper, the authors introduced binary automata neural networks, which can be considered a further development of the Hopfield model and BAM. Hopfield model is a one- state automata neural network with only one synaptic connection matrix in the alphabet. BAM is a special two-state automata neural network. In general, there can be any number of states and there can be any number of synaptic connection matrices in the alphabet. In this paper, we first systematically introduce the automata network. The original automata network is called union network in this paper. Several new types of automata networks are developed in this paper. Then we study the stability problem of the automata networks for several cases.

Citation Download Citation

Ying Liu "Stability of automata neural networks I", Proc. SPIE 1902, Nonlinear Image Processing IV, (21 May 1993); https://doi.org/10.1117/12.144764

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