title: Design and characterization of cellular automata based associative memory for pattern recognition.
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.
authors.affiliation: Department of Computer Science and Technology, Bengal Engineering College, Calcutta, West Bengal 711103, India. n_ganguly@hotmail.com
authors.forename: Niloy
authors.initials: N
authors.lastname: Ganguly
authors.forename: Pradipta
authors.initials: P
authors.lastname: Maji
authors.forename: Biplab K
authors.initials: BK
authors.lastname: Sikdar
authors.forename: P Pal
authors.initials: PP
authors.lastname: Chaudhuri
mesh_terms: D000465:Algorithms; D000818:Animals; D001244:Association; D003198:Computer Simulation; D006801:Humans; D008568:Memory / Q000502:physiology*; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D010363:Pattern Recognition, Automated