The Takagi-Sugeno fuzzy model is described by fuzzy if-then rules which represent local linear systems of the underlining nonlinear systems (Takagi & Sugeno, 1985; Tanaka et al., 1996; Tanaka & Sugeno, 1992) and thus it can describe a wide class of nonlinear systems. In the last decade, nonlinear control design methods based on Takagi-Sugeno fuzzy system have been explored. Since the stability ...

The application of fuzzy reasoning techniques and neural network structures to model-based predictive control (MPC) is studied. First, basic structures of MPC algorithms are reviewed. Then, applications of fuzzy systems of the Takagi-Sugeno type in explicit and numerical nonlinear MPC algorithms are presented. Next, many techniques using neural network modeling to improve structural or computat...

The identification of fuzzy c-regression models (FCRM) suffers from several problems characteristic of all calculusbased optimization methods, including good initialization, avoiding local minima and determining the number of clusters. This paper presents a grey-box approach that can solve the above-mentioned problems with the use of prior knowledge based constrained prototypes. The proposed ap...

A self-learning based methodology for building the rule-base of a fuzzy logic controller (FLC) is presented and verified in a practical experiment. The methodology is a simplified version of those presented in available research papers. Some aspects are intentionally ignored as they rarely appear in control system engineering and a SISO process is considered here. The fuzzy inference system obt...

Abstract. In this paper an algorithm for neuro-fuzzy identification of multivariable discrete-time nonlinear dynamical systems is proposed based on a decomposed form as a set of coupled multiple input and single output (MISO) Takagi-Sugeno (TS) neuro-fuzzy networks. An on-line scheme is formulated for modeling a nonlinear autoregressive with exogenous input (NARX) neuro-fuzzy structure from sam...

The use of Linear Matrix Inequalities and Common Quadratic Lyapunov Functions is a powerful and commonplace tool for Takagi-Sugeno fuzzy controlled system analysis and synthesis. However, in practice, few practical and performing results are available when the subsystems exhibit different input matrices, because of the strong coupling between the subsystems/subcontrollers. In this paper, this c...

A novel learning methodology based on a hybrid mechanism for training interval singleton type-2 Takagi-SugenoKang fuzzy logic systems uses recursive orthogonal least-squares to tune the type-1 consequent parameters and the steepest descent method to tune the interval type-2 antecedent parameters. The proposed hybrid-learning algorithm changes the interval type-2 model parameters adaptively to m...

This paper gives some known theoretical results about fuzzy rule-based classifiers and offers a few new ones. The ability of Takagi-Sugeno-Kang (TSK) fuzzy classifiers to match exactly and to approximate classification boundaries is discussed. The lemma by Klawonn and Klement about the exact match of a classification boundary in R (2) is extended from monotonous to arbitrary functions. Equivale...

In this paper, a modified fuzzy system modelling algorithm that incorporates Type 2 fuzzy sets, which is based on intervalvalued membership degrees rather than singleton membership degrees, is proposed. The proposed algorithm is evaluated in terms of predictive performance and determination of the significance degrees and compared with other algorithms in the literature, namely Stepwise Multipl...