interval type-2 takagi-sugeno fuzzy modeling of the chaotic systems based on variations in initial conditions

Relaxed stability conditions for Takagi-Sugeno fuzzy systems

This paper discusses conditions on stability and stabilization of continuous T-S fuzzy systems. Stability analysis is derived via non-quadratic Lyapunov function technique and LMIs (Linear Matrix Inequalities) formulation to obtain an efficient solution. The nonquadratic Lyapunov function is built by inference of quadratic Lyapunov function of each local model. We show that stability condition ...

Takagi–Sugeno Fuzzy Modeling of Skin Permeability

The skin is a major exposure route for many potentially toxic chemicals. It is, therefore, important to be able to predict the permeability of compounds through skin under a variety of conditions. Available skin permeability databases are often limited in scope and not conducive to developing effective models. This sparseness and ambiguity of available data prompted the use of fuzzy set theory ...

Takagi-Sugeno Fuzzy Modeling for Process Control

Ftsm — Fast Takagi-sugeno Fuzzy Modeling

Takagi-Sugeno type fuzzy models are widely used for model-based control and model-based fault diagnosis. They provide high accuracy with relatively small and easy to interpret models. The problem that we address in this paper is that data driven identification of such fuzzy models is computationally costly. Whereas most identification algorithms for Takagi-Sugeno models restrict the model’s gen...

Synthesis of Stable Takagi-Sugeno Fuzzy Systems

The paper deals with the problem of synthesis of a controller to the plant described by Takagi-Sugeno fuzzy model with linear input-output submodels in the consequents of rules. A procedure for design of the controller guaranteeing stability of closed-loop system based on polynomial approach is presented. The controller assignes the same characteristic polynomial for all feasible linear subsyst...

Sliding mode incremental learning algorithm for interval type-2 Takagi-Sugeno-Kang fuzzy neural networks

Type-2 fuzzy logic systems are an area of growing interest over the last years. The ability to model uncertainties and to perform under noisy conditions in a better way than type-1 fuzzy logic systems increases their applicability. A new stable on-line learning algorithm for interval type-2 Takagi–Sugeno–Kang (TSK) fuzzy neural networks is proposed in this paper. Differently from the other rece...