A consideration on the learning performances of the hierarchical structure learning automata (HSLA) operating in the general nonstationary multiteacher environment

Authors:
Norio Baba;Yoshio Mogami
Affiliations:
Information Science, Osaka Kyoiku University, Kashiwara City, Osaka Prefecture, Japan;Faculty and school of Engineering, The University of Tokushima, Tokushima, Japan
Venue:
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part III
Year:
2007

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Abstract

Learning behaviors of the hierarchical structure learning automata (HSLA) with the three representative algorithms under the nonstationary multiteacher environments are considered. Several computer simulations confirm the effectiveness of the newly developed relative reward strength algorithm (NRRSA).