Design and characterization of cellular automata based associative memory for pattern recognition

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
Niloy GangulyP Pal Chaudhuri

Abstract

This paper reports a cellular automata (CA) based model of associative memory. The model has been evolved around a special class of CA referred to as generalized multiple attractor cellular automata (GMACA). The GMACA based associative memory is designed to address the problem of pattern recognition. Its storage capacity is found to be better than that of Hopfield network. The GMACA are configured with nonlinear CA rules that are evolved through genetic algorithm (GA). Successive generations of GA select the rules at the edge of chaos. The study confirms the potential of GMACA to perform complex computations like pattern recognition at the edge of chaos.

References

Apr 1, 1982·Proceedings of the National Academy of Sciences of the United States of America·J J Hopfield
Mar 1, 1989·Physical Review A: General Physics·J BuhmannK Schulten
Jan 1, 1996·IEEE Transactions on Neural Networks·D L Wang, B Yuwono

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Citations

Nov 22, 2008·IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society·Zhigang Zeng, Jun Wang

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