Value approximation of fuzzy systems variables
Fuzzy Sets and Systems
SAC '94 Proceedings of the 1994 ACM symposium on Applied computing
Intelligent systems for engineers and scientists (2nd ed.)
Intelligent systems for engineers and scientists (2nd ed.)
A unified granular fuzzy-neuro min-max relational framework for medical diagnosis
International Journal of Advanced Intelligence Paradigms
A unified granular hybrid soft computing framework for vision engineering
International Journal of Advanced Intelligence Paradigms
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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.