In a recent paper (European Journal of Operational Research, 158, 271–292, 2004), S. Greco, B. Matarazzo and R. S lowiński have stated without proof a result characterizing binary relations on product sets that can be represented using a discrete Sugeno integral. To our knowledge, this is the first result about a fuzzy integral that applies to non-necessarily homogeneous product sets and only u...

In this paper, subtractive clustering method is combined with least squares estimation algorithms to pre-identify a type-1 Takagi-Sugeno-Kang (TSK) fuzzy model from input/output data. Then the type-2 fuzzy theory is used to expand the type-1 model to a type-2 model. A sensitivity analysis is used to ascertain how a type-1 TSK model output depends upon the pre-initialized parameters and determin...

Analysis and observer design for nonlinear systems have long been investigated, but no generally applicable methods exist as yet. A large class of nonlinear systems can be well approximated by Takagi-Sugeno fuzzy models, for which methods and algorithms have been developed to analyze their stability and to design observers. However, results obtained for Takagi-Sugeno fuzzy models are in general...

We introduce and study implicator and coimplicator integrals, and investigate their possible application in defining the possibility and necessity of fuzzy sets. First, the definition and properties of implicators and coimplicators on bounded posets are discussed. Then, in analogy with the theory of seminormed and semiconormed fuzzy integrals, implicator and coimplicator integrals are defined. ...

This paper deals with the synthesis of fuzzy state feedback controller of induction motor with optimal performance. First, the Takagi-Sugeno (T-S) fuzzy model is employed to approximate a non linear system in the synchronous d-q frame rotating with electromagnetic field-oriented. Next, a fuzzy controller is designed to stabilise the induction motor and guaranteed a minimum disturbance attenuati...

A genetic algorithm (GA) based recurrent fuzzy neural network modeling method for dynamic nonlinear chemical process is presented. The dynamic recurrent fuzzy neural network (RFNN) is constructed in terms of Takagi-Sugeno fuzzy model. The consequent part is comprised of the dynamic neurons with output feedback. The number and the parameters of membership functions in the premise part are optimi...

In this pape, a class of nonlinear time-delay systems based on the Takagi-Sugeno (T-S)fuzzy model is defined. We investigate the delay-independent stability of this model. A model-based fuzzy stabilization design utilizing the concept of the so-called “parallel distributed compensation” (PDC) is employed. The main idea of the controller design is to derive each control rule to compensate each r...