Most processes in industry are characterized by nonlinear and time-varying behavior.
Nonlinear system identification is becoming an important tool which can be used to improve …

Many authors have noted the difficulty of developing the models required for nonlinear
model predictive control (NMPC) and other nonlinear, model-based control strategies. One …

Rule-based fuzzy modeling has been recognised as a powerful technique for the modeling
of partly-known nonlinear systems. Fuzzy models can effectively integrate information from …

Presents an approach to stability analysis of fuzzy systems. The analysis is based on
Lyapunov functions that are continuous and piecewise quadratic. The approach exploits the …

Abstract Predictive Maintenance (PdM) is one of the core innovations in recent years that
sparks interest in both research and industry. While researchers develop more and more …

Much has been written about the general difficulty of developing the models required for
model-based control of processes whose dynamics exhibit signif icant nonlinearity (for …

This paper presents a new multi-stage genetic programming (MSGP) strategy for modeling
nonlinear systems. The proposed strategy is based on incorporating the individual effect of …

Abstract Fuzzy model identification is an effective tool for the approx-imation of uncertain
nonlinear systems on the basis of measured data. The identification of a fuzzy model using …

Projection to latent structures (PLS) has been shown to be a robust multivariate linear
regression technique for the analysis and modelling of noisy and highly correlated data. It …

During the past few years two principally different approaches to the design of fuzzy
controllers have emerged: heuristics-based design and model-based design. The main …