Fuzzy modeling in terms of surprise

This paper presents a new approach to fuzzy modeling based on the concept of surprise. The new concept is related to the traditional membership function by an antitone transformation. Advantages of the surprise approach include: 1. It has a consistent semantic interpretation. 2. It allows the joint use of quantitative and qualitative knowledge, using simple rules of logic. 3. It is a direct extension of (and allows combination with) the least-squares approach to reconciling conflicting approximate numerical data. 4. It is ideally suited to optimization under imprecise or conflicting goals, specified by a combination of soft and hard interval constraints. 5. It gives a straightforward approach to constructing families of functions consistent with fuzzy associative memories as used in fuzzy control, with tuning parameters (reflecting linguistic ambiguity) that can be adapted to available performance data. Π2002 Elsevier Science B.V. All rights reserved.