Evolving systems unfolds from the interaction and cooperation between systems with
adaptive structures, and recursive methods of machine learning. They construct models and …

A fairly general class of nonlinear plants can be modeled as fuzzy systems, ie, as a time-
varying convex combination of “vertex” linear systems. As many linear LMI control results …

Many problems in decision making, monitoring, fault detection, and control rely on the
knowledge of state variables and time-varying parameters that are not directly measured by …

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

A comprehensive coverage of emerging and current technology dealing with heterogeneous
sources of information, including data, design hints, reinforcement signals from external …

In order to improve the real time forecasting of foods, this paper proposes a modified Takagi
Sugeno (T–S) fuzzy inference system termed as threshold subtractive clustering based …

In this paper, we propose a new methodology for learning evolving fuzzy systems (EFS) from
data streams in terms of on-line regression/system identification problems. It comes with …

Massively parallel, throughput-oriented systems such as graphics processing units (GPUs)
offer high performance for a broad range of programs. They are, however, complex to …

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 paper introduces a class of evolving fuzzy rule-based system as an approach for
multivariable Gaussian adaptive fuzzy modeling. The system is an evolving Takagi-Sugeno …