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Toward a theory of validation of hybrid MinMax FuzzyNeuro systems

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1 Author(s)
Mokhtar Beldjehem ; Département de génie informatique et génie logiciel (GIGL), École Polytechnique de Montréal, Campus de l¿Université de Montréal, Case postale 6079-Succursale Centre Ville, Québec, Canada, H3C 1K3

The validation and verification (V&V) of hybrid fuzzyneuro (HFN) or hybrid neurofuzzy (HNF) systems becomes of increasing concern as these systems are fielded and embedded in the every day operations of medical diagnosis, pattern recognition, fuzzy control and other industries-particularly so when life-critical and environment-critical aspects are involved. We provide in this paper a V&V perspective on the nature of HFN components, an appropriate life-cycle, and applicable systematic formal testing approaches. We consider why HFN V&V may be both easier and harder than traditional means, and we conclude with a series of practical V&V guidelines. Validation of HFN systems brings us to a systematic study of value approximation performed during the inference phase. It is accepted that generalization capability is proportional to value approximation.

Published in:

2008 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications

Date of Conference:

14-16 July 2008