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 language | English (US) |
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| Title of host publication | Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS |
| Publisher | IEEE Computer Society |
| Pages | 317-321 |
| Number of pages | 5 |
| State | Published - 1995 |
| Event | Proceedings 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 1995 → Sep 20 1995 |
Other
| Other | Proceedings of the 3rd International Symposium on Uncertainty Modeling and Analysis and Annual Conference of the North American Fuzzy Information Processing Society, (ISUMA - NAFIPS'95) |
|---|---|
| City | College Park, MD, USA |
| Period | 9/17/95 → 9/20/95 |
ASJC Scopus subject areas
- General Engineering