Automatic Construction of Fuzzy Rules for Modelling and Prediction of the Central Nervous System
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
Reasoning under uncertainty with FIR methodology
International Journal of Approximate Reasoning
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The aim of this research is to develop a strategy of reasoning under uncertainty in the context of the Fuzzy Inductive Reasoning methodology. This methodology allows the prediction of systems behavior by means of two different schemes. The first one corresponds to a pattern prediction scheme, based exclusively on pattern rules. The second one corresponds to a purely Sugeno inference system, i.e. Sugeno prediction scheme. The Sugeno fuzzy rules are automatically extracted from the pattern rules producing a compact representation of the system modelled. In this paper a mixed pattern/fuzzy rules scheme is studied to deal with uncertainty in such a way that the best of both perspectives is used. The proposed scheme is applied to a real biomedical system, i.e. the central nervous system control of the cardiovascular system.