In order to model complex industrial processes, this work studies the identification of linear
parameter varying (LPV) models with two scheduling variables. The LPV model is …

Neural networks have been widely used to model nonlinear systems for control. The curse of
dimensionality and lack of transparency of such neural network models has forced a shift …

This paper proposes a new approach of observer design for nonlinear systems described by
a Takagi-Sugeno model. Its main contribution concerns models with premise variables …

An identification method is described to determine a weighted combination of local linear
state-space models from input and output data. Normalized radial basis functions are used …

Multiple models are recognised by their abilities to accurately describe nonlinear dynamic
behaviours of a wide variety of nonlinear systems with a tractable model in control …

This paper presents a new algorithm for fuzzy c-regression model clustering. The proposed
methodology is based on adding a second regularization term in the objective function of a …

The objective of this paper is to describe a new method for identification of a continuous-time
multi-input and multi-output bilinear system. The approach is to make judicious use of the …

This paper presents the application of an online identification neural technique to the
prediction of the in-situ daily performance of solar collectors. First, it is shown that the use of …

It is difficult to realize adaptive control for some complex nonlinear processes which are
operated in different environments and when operation conditions are changed frequently …

This paper is devoted to the problem of observer design for synchronization of nonlinear
fractional-order chaotic systems described by the Takagi–Sugeno fuzzy model. We propose …