In this article some aspects of fuzzy modeling are discussed in connection with nonlinear
system identification and control design. Methods for constructing fuzzy models from process …

Fostered by a growing interest in nonlinear control theory and catalyzed by the discovery in
the early 1980s of the exact conditions under which a nonlinear plant can be linearized by …

A nonlinear black-box structure for a dynamical system is a model structure that is prepared
to describe virtually any nonlinear dynamics. There has been considerable recent interest in …

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 …

A nonlinear black-box structure for a dynamical system is a model structure that is prepared
to describe virtually any nonlinear dynamics. There has been considerable recent interest in …

The magnetorheological (MR) damper is a newly developed semiactive control device that
possesses unique advantages such as low power requirement and adequately fast …

In Chapter 1 some fundamental results on the approximation capabilities of classical MLPs
in the set of continuous, real valued functions, were given. This chapter extends those …

This paper proposes a sparse Bayesian treatment of deep neural networks (DNNs) for
system identification. Although DNNs show impressive approximation ability in various …

Due to the advancement in computational intelligence and machine learning methods and
the abundance of data, there is a surge in the use of data-driven models in different …

Two established approaches to engineer adaptive systems are architecture-based
adaptation that uses a Monitor-Analysis-Planning-Executing (MAPE) loop that reasons over …