Meshfree approximation methods are a relatively new area of research, and there are only a
few books covering it at present. Whereas other works focus almost entirely on theoretical …

Abstract Radial Basis Function neural networks (RBFNNs) represent an attractive alternative
to other neural network models. One reason is that they form a unifying link between function …

In today's industrial systems, economic markets, life and health-care sciences fuzzy systems
play an important role in many application scenarios such as system identification, fault …

Artificial immune systems (AIS) can be defined as computational systems inspired by
theoretical immunology, observed immune functions, principles and mechanisms in order to …

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

Conventional model-based data processing methods are computationally expensive and
require experts' knowledge for the modelling of a system. Neural networks are a model-free …

The degree of vagueness of variables, process description, and automation functions is
considered and is shown. Where quantitative and qualitative knowledge is available for …

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 …

A presentation of techniques in advanced process modelling, identification, prediction, and
parameter estimation for the implementation and analysis of industrial systems. The authors …

Radial basis function networks (RBFNs) have gained widespread appeal amongst
researchers and have shown good performance in a variety of application domains. They …