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
Genetic approaches to search for computing patterns in cellular automata
IEEE Computational Intelligence Magazine
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 the error correcting capability of an associative memory model built around the sparse network of cellular automata (CA). Analytical formulation supported by experimental results has demonstrated the capability of CA based sparse network to memorize unbiased patterns while accommodating noise. The desired CA are evolved with an efficient formulation of simulated annealing (SA) program. The simple, regular, modular, and cascadable structure of CA based associative memory suits ideally for design of low cost high speed online pattern recognizing machine with the currently available VLSI technology.