Hierarchical Discretized Pursuit Nonlinear Learning Automata with Rapid Convergence and High Accuracy

Authors:
G. I. Papadimitriou
Affiliations:
-
Venue:
IEEE Transactions on Knowledge and Data Engineering
Year:
1994

Citing 3
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Abstract

A new absorbing multiaction learning automaton that is epsilon-optimal is introduced. It is a hierarchical discretized pursuit nonlinear learning automaton that uses a new algorithm for positioning the actions on the leaves of the hierarchical tree. The proposed automaton achieves the highest performance (speed of convergence, central processing unit (CPU) time, and accuracy) among all the absorbing learning automata reported in the literature up to now. Extensive simulation results indicate the superiority of the proposed scheme. Furthermore, it is proved that the proposed automaton is epsilon-optimal in every stationary stochastic environment.