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1

Exploring Cycle Structures of Additive Cellular Automata

Ganguly N., Sikdar B.K., Chaudhuri P.P.

Fundamenta Informaticae

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2008

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Vol. 87, nr 2

137-154

EN

This paper reports the complete characterization of additive cellular automaton (ACA) that employs xor and xnor logic to realize its next state function. Compared to linear cellular automaton (LCA) [], which employs only xor logic in its next state function, an ACA displays much more wider varieties of state transition behavior leading to enhanced computing power. An analytical framework is developed to characterize the cyclic vector subspaces of an ACA that can be derived from careful analysis of the vector subspaces covered by the LCA. A scheme is proposed to explore the ACA structures having different state transition behavior than that of its LCA counterpart. The reported theoretical analysis justifies the nature of differences.

2

RBFFCA: A Hybrid Pattern Classifier Using Radial Basis Function and Fuzzy Cellular Automata

Maji P., Chaudhuri P.P.

Fundamenta Informaticae

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2007

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Vol. 78, nr 3

369-396

EN

A hybrid learning algorithm, termed as RBFFCA, for the solution of classification problems with real valued inputs is proposed. It comprises an integration of the principles of radial basis function (RBF) and fuzzy cellular automata (FCA). The FCA has been evolved through genetic algorithm (GA) formulation to perform pattern classification task. The versatility of the proposed hybrid scheme is illustrated through its application in diverse fields. Simulation results conducted on benchmark database show that the hybrid pattern classifier achieves excellent performance both in terms of classification accuracy and learning efficiency. Extensive experimental results supported with analytical formulation establish the effectiveness of RBFFCA based pattern classifier and prove it as an efficient and cost-effective alternative for the classification problem.

3

Theory and Application of Cellular Automata For Pattern Classification

Maji P., Shaw C., Ganguly N., Sikdar B.K., Chaudhuri P.P.

Fundamenta Informaticae

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2003

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Vol. 58, nr 3,4

321--354

EN

This paper presents the theory and application of a high speed, low cost pattern classifier. The proposed classifier is built around a special class of sparse network referred to as Cellular Automata (CA). A specific class of CA, termed as Multiple Attractor Cellular Automata (MACA), has been evolved through Genetic Algorithm (GA) formulation to perform the task of pattern classification. The versatility of the classification scheme is illustrated through its application in three diverse fields - data mining, image compression, and fault diagnosis. Extensive experimental results demonstrate better performance of the proposed scheme over popular classification algorithms in respect of memory overhead and retrieval time with comparable classification accuracy. Hardware architecture of the proposed classifier has been also reported.