Variable Hierarchical Structure Learning Automata with an S-model Non-stationary Random Environment at Each Level

Abstract

The purpose of this paper is to construct a learning algorithm for variable hierarchical structure learning automata with an S-model nonstationary random environment at each level. The learning propertiy of our algorithm is considered theoretically, and it is proved that the probability finding the optimal objective path can be approached 1 as much as possible by using our algorithm. In numerical simulation, the usefulness of our algorithm is shown.