A MATHEMATICAL FRAMEWORK FOR CELLULAR LEARNING AUTOMATA

Hamid Beigy () and M. R. Meybodi ()
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Hamid Beigy: Computer Engineering Department, Sharif University of Technology, Tehran, Iran;
M. R. Meybodi: Computer Engineering Department, Amirkabir University of Technology, Tehran, Iran;

Advances in Complex Systems (ACS), 2004, vol. 07, issue 03n04, 295-319

Abstract: The cellular learning automata, which is a combination of cellular automata, and learning automata, is a new recently introduced model. This model is superior to cellular automata because of its ability to learn and is also superior to a single learning automaton because it is a collection of learning automata which can interact with each other. The basic idea of cellular learning automata, which is a subclass of stochastic cellular learning automata, is to use the learning automata to adjust the state transition probability of stochastic cellular automata. In this paper, we first provide a mathematical framework for cellular learning automata and then study its convergence behavior. It is shown that for a class of rules, called commutative rules, the cellular learning automata converges to a stable and compatible configuration. The numerical results also confirm the theoretical investigations.

Keywords: Cellular learning automata; cellular automata; learning automata; interconnected automata (search for similar items in EconPapers)
Date: 2004
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Citations: View citations in EconPapers (1)

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DOI: 10.1142/S0219525904000202

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