Characterization of Non-linear Cellular Automata Model for Pattern Recognition

Ganguly, Niloy; Maji, Pradipta; Das, Arijit; Sikdar, Biplab K.; Chaudhuri, P.P.

doi:10.1007/3-540-45631-7_29

Cited by 6 publications

(5 citation statements)

References 4 publications

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“…5) denotes the probability of occurrence of distinct neighborhood configurations for the th cell in the patterns to be learnt. 6) denotes the number of configuration pairs of th cell with HD as . denotes the error correcting capability of the th cell with number of distinct neighborhood configurations in the patterns to be learnt.…”

Section: )mentioning

confidence: 99%

“…For number of patterns can vary from 2 to . Hence, the probability of occurrence of distinct configurations at the th position is given by (6) where…”

Section: B Rule Space Of Gmaca With Multiple Attractor Basinsmentioning

confidence: 99%

“…The model is built around a general class of CA termed as generalized multiple attractor CA 1083-4427/03$17.00 © 2003 IEEE (GMACA). This specific class of CA displays encouraging results in the field of pattern recognition [5], [6], [8], [14], [15]. While in [8] we have mainly dealt with memorizing capacity of CA based associative memory, in the present one we focus our attention on its error correcting capability.…”

Section: Introductionmentioning

confidence: 99%

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Error correcting capability of cellular automata based associative memory

Maji

Ganguly

Chaudhuri

2003

IEEE Trans. Syst., Man, Cybern. A

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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.

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Section: )mentioning

confidence: 99%

“…For number of patterns can vary from 2 to . Hence, the probability of occurrence of distinct configurations at the th position is given by (6) where…”

Section: B Rule Space Of Gmaca With Multiple Attractor Basinsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Error correcting capability of cellular automata based associative memory

Maji

Ganguly

Chaudhuri

2003

IEEE Trans. Syst., Man, Cybern. A

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“…The design of pattern classifiers using CAs are widely reported in literature where synchronous CAs have been used [32,[39][40][41][42]. It should be mentioned here that, the issue of pattern classification was not previously tackled with ACAs.…”

Section: Introductionmentioning

confidence: 99%

Asynchronous cellular automata and pattern classification

2016

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This paper designs an efficient two-class pattern classifier utilizing asynchronous cellular automata (ACAs). The two-state three-neighborhood one-dimensional ACAs that converge to fixed points from arbitrary seeds are used here for pattern classification. To design the classifier, we first identify a set of ACAs that always converge to fixed points from any seeds with following properties -(1) each ACA should have at least two but not huge number of fixed point attractors, and (2) the convergence time of these ACAs are not to be exponential. In order to address the first issue, we propose a graph, coined as fixed point graph of an ACA that facilitates in counting the fixed points. We further perform an experimental study to estimate the convergence time of ACAs, and find that there are some convergent ACAs which demand exponential convergence time. Finally, we find that there are 71 (out of 256) ACAs which can be effective candidates as pattern classifier. We use each of the candidate ACAs on some standard data sets, and observe the effectiveness of each ACAs as pattern classifier. It is observed that the proposed classifier is very competitive and performs reliably better than many standard existing algorithms.

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“…The special class of CA, referred to as GMACA [15] (Generalized Multiple Attractor Cellular Automata), is employed for the design. The desired CA model, evolved through an efficient implementation of genetic algorithm, is found to be at the edge of chaos.…”

Section: Generalized Multiple Attractor Camentioning

confidence: 99%