Learning Automata with 2-State Bayesian Estimators

Abstract

This paper presents a novel learning automaton, βtype, which consists of 2-state Bayesian estimators. The β-type learning automaton is presently among the fastest learning automata known, which was proposed in our earlier works. However, compared with the βtype learning automaton and the conventional learning automata, the β-type learning automaton deteriorates from the viewpoint of memory usage and other resources, for example, since computational and energy resources of some applications are limited, such as the wireless sensor networks, reducing memory footprint and performance optimization are very important issues. So, in this study, we propose the β-type learning automaton with minimum resources, 2-state Bayesian estimators. Then, the efficiency of proposed β-type learning automaton is shown through several simulation results under some random environments.

Conditional Optimality of Learning Automata with 2-state Bayesian Estimators

“…The β-type LA employs the minimum configured Bayesian estimators, and shows appropriate property. Compared with the proposed β-type LA and the conventional LAs, the β-type one achieve well balanced performance through some simulation results [10].…”

“…For the above reasons, we have proposed the βtype LA with minimum resources, 2-state Bayesian estimators [10]. In this study, we show the sufficient condition for conditional optimality of the β-type LA with 2-state Bayesian estimators.…”