The paper is concerned with robust stability criteria for Takagi- Sugeno (T-S) fuzzy systems with distributed delays and time delay in the leakage term. By exploiting a model transformation, the system is converted to one of the neutral delay system. Global robust stability result is proposed by a new Lyapunov-Krasovskii functional which takes into account the range of delay and by making use o...

The identification of nonlinear multi-input multi-output (MIMO) processes is important and challenging problem. Fuzzy systems have been effectively used to identify complex nonlinear dynamical systems, but mostly single-input single-output systems are considered. This paper presents a compact Takagi-Sugeno fuzzy model that can be effectively used to represent MIMO dynamical systems. For the ide...

The “fuzzy dot” (or fuzzy relation) representation of fuzzy rules in fuzzy rule based systems, in case of classical fuzzy reasoning methods (e.g. the Zadeh-MamdaniLarsen Compositional Rule of Inference (CRI) (Zadeh, 1973) (Mamdani, 1975) (Larsen, 1980) or the Takagi Sugeno fuzzy inference (Sugeno, 1985) (Takagi & Sugeno, 1985)), are assuming the completeness of the fuzzy rule base. If there are...

This paper investigates some properties of Takagi-Sugeno (T-S) fuzzy Hopfield neural networks. First, we prove that there exists a unique solution of the T-S fuzzy Hopfield neural network. Second, we determine a condition for input-to-state stability (ISS) of the T-S fuzzy Hopfield neural network. These results will be useful to analyze dynamic behavior of fuzzy neural networks.

In this chapter, modeling and simulation of a Takagi–Sugeno based fuzzy logic control strategy in order to control one of the most important parameters of the IM, viz., speed, is presented. In the control of IMs, FLCs play a very important role. In this context, a novel FLC is developed based on the Takagi–Sugeno principles. This method gives very good response compared to other methods such as...

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...

A type of flexible models in the form of a neural network (NN) with evolving structure is treated in the paper. We refer to models with amorphous structure as flexible models. There is a close link between different types of flexible models: fuzzy models, fuzzy NN, and general regression model. All of them are proven universal approximators and some of them (Takagi-Sugeno fuzzy model with singl...

Fuzzy-Systeme sind im Allgemeinen statische Systeme. Des Weiteren lassen sich aber auch dynamische Systeme direkt durch Fuzzy-Funktionen und Regeln beschreiben. Ihre Dynamik ist dabei inhärent und wird nicht durch das Gewichten von dynamischen Modellen wie im Falle von Takagi–Sugeno–Kang-Systemen erreicht, sondern durch direkte Rückführung der Ausgangsgrößen eines Fuzzy-Systems. In diesem Beitr...