Hierarchical discretized pursuit nonlinear learning automata with rapid convergence and high accuracy

Author

Papadimitriou, Georgios I.

Author_Institution

Dept. of Comput. Eng., Patras Univ., Greece

Volume

6

Issue

4

fYear

1994

fDate

8/1/1994 12:00:00 AM

Firstpage

654

Lastpage

659

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

Keywords

convergence; finite automata; hierarchical systems; nonlinear systems; unsupervised learning; absorbing multiaction learning automaton; discretized pursuit nonlinear learning automata; epsilon-optimal learning; hierarchical tree; nonlinear output function; positioning algorithm; pursuit learning; rapid convergence; stationary stochastic environment; Collision avoidance; Convergence; Cybernetics; Data communication; Learning automata; Notice of Violation; Pursuit algorithms; Routing; Stochastic processes; USA Councils;

fLanguage

English

Journal_Title

Knowledge and Data Engineering, IEEE Transactions on

Publisher

ieee

ISSN

1041-4347

Type

jour

DOI

10.1109/69.298184

Filename

298184