Fuzzy logic, neural networks, and evolutionary computation are the core methodologies of
soft computing (SC). SC is causing a paradigm shift in engineering and science fields since …

Through the past 20 years, the framework of Linear Parameter-Varying (LPV) systems has
become a promising system theoretical approach to handle the control of mildly nonlinear …

Dynamic Takagi-Sugeno fuzzy models are not always easy to interpret, in particular when
they are identified from experimental data. It is shown that there exists a close relationship …

Advanced engine control systems require accurate dynamic models of the combustion
process, which are substantially nonlinear. This contribution presents the application of fast …

The problem of identifying the parameters of the constituent local linear models of Takagi-
Sugeno fuzzy models is considered. In order to address the tradeoff between global model …

The design of a new motion controller for pneumatic actuators using local linear models is
described with an application to a two-way servo-pneumatic drive under a time variable …

This paper uses Kalman filter theory to design a state estimator for noisy discrete time
Takagi–Sugeno (T–S) fuzzy models. One local filter is designed for each local linear model …

A global model structure is developed for parametrization and identification of a general
class of Linear Parameter-Varying (LPV) systems. By using a fixed orthonormal basis …

In this paper it is shown that the dynamics of a conventional type of blended multiple model
system are only weakly related to the local models from which it is formed. A novel class of …

Parametric modelling principals such as neural networks, fuzzy models and multiple model
techniques have been proposed for modelling of nonlinear systems. Research effort has …