Functional equivalence of radial basis function (RBF) networks and a class of fuzzy inference systems is considered. The class of fuuy systems based on the Takagi-Sugeno model is referred to as TS-model of fuzzy inference. From the abstract mathematical viewpoint the functional equivalence between radial basis function networks and fuzzy inference systems is already shown. However, from the vie...

This article describes an approach to automatically build a Takagi-Sugeno fuzzy model (TSK-model) based on a set of input-output data (system identification). Identifying rule-based fuzzy models consists of two parts: structure modeling, i. e. determining the number of rules and input variables involved respectively, and parameter optimization, i. e. optimizing the rules consequences and the lo...

Shifted-Chebyshev-series approach (SCSA) and hybrid Taguchi-genetic algorithm (HTGA) were integrated into a Takagi-Sugeno (TS) fuzzy model nonlinear human immunodeficiency virus (HIV) dynamics. Since only the algebraic computations in SCSA were applied in the TS fuzzy model-based dynamic equations, the proposed model simplified the problem by transforming it into a static optimization problem r...

This paper introduces a new methodology for the damage assessment of existing-transmission structures using six layers, zero order Sugeno model. The model is a hybrid fuzzy-neural system that combines the power of neural networks and fuzzy systems. It is a learning expert system that finds the parameters of the fuzzy sets and fuzzy rules by exploiting approximation techniques from neural networ...

Fuzzy theory provides a rigorous, flexible approach to the problem of defining and computing. Therefore, to facilitate decision-making in geographic information system (GIS), the graph layer indicator and the Takagi Sugeno (TS) fuzzy model have to be integrated. This study explains several versions of the TS fuzzy model based on fuzzy theory and fuzzy operation. An inference model is constructe...

AbstructThis paper presents a gain scheduling based approach to the design of fuzzy controllers. We demonstrate that the Sugeno fuzzy controller is in fact a special case of gain scheduling where the feedback gains are adjusted (“scheduled”) via membership functions in fuzzy if-then rules. When the desired gains at various operating points are available, we can apply the ANFIS learning algorith...

The mountain car problem is a well-known task, often used for testing reinforcement learning algorithms. It is a problem with real valued state variables, which means that some kind of function approximation is required. In this paper, three reinforcement learning architectures are compared on the mountain car problem. Comparison results are presented, indicating the potentials of the actor-onl...

In this paper we consider the control problem of Dynamic Positioning Systems for Ships (DPSS). It interests as a case which study in nonlinear controller design because the model exhibits nonlinear interaction in three degrees of freedom (surge, sway, and yaw) by means of main propellers aft of the ship. The DPSS system considered in this paper is modeled by a Takagi-Sugeno (T-S) type fuzzy mod...

The main objective of this paper is to expound the singular-value-decomposition (SVD)-based reduction technique proposed to single-input–single-output Takagi-Sugeno (TS) fuzzy models to multivariable cases. The use of higher order singular value decomposition is proposed in this paper for the complexity reduction of multiple-input–single-output TS fuzzy model approximation. A detailed illustrat...