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

Major assumptions in computational intelligence and machine learning consist of the
availability of a historical dataset for model development, and that the resulting model will, to …

Abstract Concept drift is a common and important issue in streaming data analysis and
mining. Thus far, many concept drift detection methods have been proposed but may not be …

This chapter provides an all-round picture of the development and advances in the fields of
evolving fuzzy systems (EFS) and evolving neuro-fuzzy systems (ENFS) which have been …

In this article, a new dynamic merging approach for incrementally evolving clustering is
presented. This means that the cluster partitions are incrementally learned on-line from …

This article presents a new design of experiment approach based on an evolving neuro-
fuzzy model. The input of the process is proposed by a space-filling method that uses a …

This paper presents a new approach for identifying interval fuzzy models, which enables
estimating fuzzy model structures, parameters, and upper and lower bounds simultaneously …

Anomaly detection in todays industrial environments is an ambitious challenge to detect
possible faults/problems which may turn into severe waste during production, defects, or …

Fuzzy C-means (FCM) is a kind of classic cluster method, which has been widely used in
various fields, such as image segmentation and data mining. Euclidean distance is a …

This paper presents a new approach to neuro-fuzzy model identification based on a filtered
recursive least squares method combined with an incrementally evolving Gaussian …