Neuro-fuzzy systems have attracted the growing interest of researchers in various scientific
and engineering areas due to its effective learning and reasoning capabilities. The neuro …

Chaotic time series widely exists in nature and society (eg, meteorology, physics,
economics, etc.), which usually exhibits seemingly unpredictable features due to its inherent …

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 In 2011, the “ADHD-200 Global Competition” was held with the aim of identifying
biomarkers of attention-deficit/hyperactivity disorder from resting-state functional magnetic …

The prediction of time series has both the theoretical value and practical significance in
reality. However, since the high nonlinear and noises in the time series, it is still an open …

Evolving fuzzy classifiers (EFCs) have achieved immense success in dealing with
nonstationary data streams because of their flexible characteristics. Nonetheless, most real …

The central purpose of this survey is to provide readers an insight into the recent advances
and challenges in on-line active learning. Active learning has attracted the data mining and …

Concept-cognitive learning (CCL) is an emerging field of concerning incremental concept
learning and dynamic knowledge processing in the context of dynamic environments …

Abstract Socially Assistive Robots (SARs) are increasingly used in dementia and elderly
care. In order to provide effective assistance, SARs need to be personalized to individual …

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 …