Essentials of fuzzy modeling and control
Essentials of fuzzy modeling and control
Nonlinear black-box modeling in system identification: a unified overview
Automatica (Journal of IFAC) - Special issue on trends in system identification
Nonlinear black-box models in system identification: mathematical foundations
Automatica (Journal of IFAC) - Special issue on trends in system identification
Development of a systematic methodology of fuzzy logic modeling
IEEE Transactions on Fuzzy Systems
A fuzzy-logic-based approach to qualitative modeling
IEEE Transactions on Fuzzy Systems
On the robustness of Type-1 and Interval Type-2 fuzzy logic systems in modeling
Information Sciences: an International Journal
An expert fuzzy cognitive map for reactive navigation of mobile robots
Fuzzy Sets and Systems
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This paper addresses the robustness characteristics of the fuzzy inference mechanism in terms of maximum deviation of the fuzzy and crisp output as a result of the deviation of the input membership grades. A formulation that introduces several parameters into the fuzzy reasoning process provides a suitable means to adjust the robustness of the inference engine. The effect of each of these parameters is investigated and specific guidelines for assigning their range are developed to achieve maximum robustness. The maximum possible robustness is achieved by reducing the sensitivity of the inference mechanism to input variation to a satisfactory level. This feature will improve the generalization capability of fuzzy-logic models as illustrated with a well-known example from the literature.