Mathematical modeling and simulation methods are important tools in studying various
processes in science and engineering. In the current review, we focus on the applications of …

Hybrid semi-parametric models consist of model structures that combine parametric and
nonparametric submodels based on different knowledge sources. The development of a …

This is a tutorial on the mathematical theory and process control applications of linear matrix
inequalities (LMIs) and bilinear matrix inequalities (BMIs). Many convex inequalities …

In the past decade, the control of nonlinear systems has received considerable attention in
both academia and industry. The recent interest in the design and analysis of nonlinear …

Dataclusteringisacommontechniqueforstatis…, whichisusedin many? elds, including
machine learning, data mining, pattern recognition, image analysis and bioinformatics …

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 …

Smart manufacturing (SM) is a new paradigm that allows manufacturing to enter its fourth
revolution by exploiting state-of-the art sensing, communication and computation as the …

The inherent time-varying nonlinearity and complexity usually exist in chemical processes.
The design of control structure should be properly adjusted based on the current state. In …

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 …

This monograph systematically presents the existing identification methods of nonlinear
systems using the block-oriented approach It surveys various known approaches to the …