Incentive games for neuro-fuzzy control

A. Mete Cakmakci, Can Isik

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper we introduce a two-level modular neuro-fuzzy network based on incentive games where the modules are organized as autonomous local optimizers in a leader-follower game hierarchy. Incentive-reaction pairs are used as a measure for the capacity and responsiveness assessment of each follower module. Learning within the follower modules is performed in a traditional error-based manner (e.g., backpropagation) The allocation of targets and incentives to each follower module, on the other hand is independent of connection weights; incentive games are used for that purpose. Two important advantages of the new architecture are its physically significant follower module outputs and the context-based enhancement it makes to backpropagation.

Original languageEnglish (US)
Title of host publicationAnnual Conference of the North American Fuzzy Information Processing Society - NAFIPS
PublisherIEEE Computer Society
Pages317-321
Number of pages5
StatePublished - 1995
EventProceedings of the 3rd International Symposium on Uncertainty Modeling and Analysis and Annual Conference of the North American Fuzzy Information Processing Society, (ISUMA - NAFIPS'95) - College Park, MD, USA
Duration: Sep 17 1995Sep 20 1995

Other

OtherProceedings of the 3rd International Symposium on Uncertainty Modeling and Analysis and Annual Conference of the North American Fuzzy Information Processing Society, (ISUMA - NAFIPS'95)
CityCollege Park, MD, USA
Period9/17/959/20/95

ASJC Scopus subject areas

  • Engineering(all)

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