Abstract Integrated System Health Management (ISHM) is a promising technology that fuses
sensor data and historical state-of-health information of components and subsystems to …

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 …

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 …

This special issue explores big data analytics and applications for logistics and supply chain
management by examining novel methods, practices, and opportunities. The articles present …

The main reason of the extensive usage of the fuzzy systems in many branches of science is
their approximation ability. In this paper, an interval type-3 fuzzy system (IT3FS) is proposed …

The age of online data stream and dynamic environments results in the increasing demand
of advanced machine learning techniques to deal with concept drifts in large data streams …

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 …

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 …

In this paper the optimization of type-2 fuzzy inference systems using genetic algorithms
(GAs) and particle swarm optimization (PSO) is presented. The optimized type-2 fuzzy …

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