Cellular learning automata with external input and its applications in pattern recognition

Abstract

Cellular learning automata (CLA) which has been introduced recently, is a combination of cellular automata (CA) and learning automata (LA). A CLA is a CA in which a LA is assigned to its every cell. The LA residing in each cell determines the state of the cell on basis of its action probability vector. Like CA, there is a local rule that CLA operates under it. In this paper we introduce a new model of CLA in which each cell gets an external input vector from the environment in addition to reinforcement signal, so this model can work in non-stationary environments. Then two applications of the new model on image segmentation and clustering are given, and the results show that the proposed algorithm outperforms the similar algorithms.