Non-uniform cellular automata based associative memory: Evolutionary design and basins of attraction
Information Sciences: an International Journal
RBFFCA: A Hybrid Pattern Classifier Using Radial Basis Function and Fuzzy Cellular Automata
Fundamenta Informaticae - Special issue on DLT'04
On Characterization of Attractor Basins of Fuzzy Multiple Attractor Cellular Automata
Fundamenta Informaticae
Image ordering by cellular genetic algorithms with TSP and ICA
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
On Characterization of Attractor Basins of Fuzzy Multiple Attractor Cellular Automata
Fundamenta Informaticae
RBFFCA: A Hybrid Pattern Classifier Using Radial Basis Function and Fuzzy Cellular Automata
Fundamenta Informaticae - Special issue on DLT'04
An improved multiple-attractor cellular automata classifier with a tree frame based on CART
Computers & Mathematics with Applications
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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.