Linguistic fuzzy modelling, developed by linguistic fuzzy rule-based systems, allows us to
deal with the modelling of systems by building a linguistic model which could become …

Artificial neural networks (ANN) have been widely used and have achieved remarkable
achievements. However, neural networks with high accuracy and good performance often …

We present a measure of performance (MOP) for identification algorithms based on the
evidential theory of Dempster–Shafer. As an MOP, we introduce a principled distance …

In recent years, a great number of publications have explored the use of genetic algorithms
as a tool for designing fuzzy systems. Genetic Fuzzy Systems explores and discusses this …

Fuzzy inference systems (FIS) are widely used for process simulation or control. They can be
designed either from expert knowledge or from data. For complex systems, FIS based on …

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 …

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

Fuzzy modeling of high-dimensional systems is a challenging topic. This paper proposes an
effective approach to data-based fuzzy modeling of high-dimensional systems. An initial …

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 local statistical approach for fault detection and isolation is applied to fuzzy models
validation. The method detects the inconsistencies between a fuzzy rule base and the …