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The aim of this research is to develop a reasoning under uncertainty strategyin the context of the Fuzzy Inductive Reasoning (FIR) methodology. FIR emergedfrom the General Systems Problem Solving developed by G. Klir. It is a datadriven methodology based on systems behavior rather than on structuralknowledge. It is a very useful tool for both the modeling and the prediction ofthose systems for which no previous structural knowledge is available. FIRreasoning is based on pattern rules synthesized from the available data. Thesize of the pattern rule base can be very large making the prediction processquite difficult. In order to reduce the size of the pattern rule base, it ispossible to automatically extract classical Sugeno fuzzy rules starting fromthe set of pattern rules. The Sugeno rule base preserves pattern rulesknowledge as much as possible. In this process some information is lost butrobustness is considerably increased. In the forecasting process either thepattern rule base or the Sugeno fuzzy rule base can be used. The first optionis desirable when the computational resources make it possible to deal with theoverall pattern rule base or when the extracted fuzzy rules are not accurateenough due to uncertainty associated to the original data. In the secondoption, the prediction process is done by means of the classical Sugenoinference system. If the amount of uncertainty associated to the data is small,the predictions obtained using the Sugeno fuzzy rule base will be veryaccurate. In this paper a mixed pattern/fuzzy rules strategy is proposed todeal with uncertainty in such a way that the best of both perspectives is used.Areas in the data space with a higher level of uncertainty are identified bymeans of the so-called error models. The prediction process in these areasmakes use of a mixed pattern/fuzzy rules scheme, whereas areas identified witha lower level of uncertainty only use the Sugeno fuzzy rule base. The proposedstrategy is applied to a real biomedical system, i.e., the central nervoussystem control of the cardiovascular system.

Dealing with uncertainty in fuzzy inductive reasoning methodology