This paper presents a review of the central theories involved in hybrid models based on
fuzzy systems and artificial neural networks, mainly focused on supervised methods for …

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 …

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

A recurrent fuzzy neural cerebellar model articulation network (RFNCMAN) fault-tolerant
control of a six-phase permanent magnet synchronous motor (PMSM) position servo drive is …

In real applications learning algorithms have to address several issues such as, huge
amount of data, samples which arrive continuously and underlying data generation …

Deep learning techniques have been widely used in traffic flow prediction. They can perform
much better than shallow models. However, most existing deep learning models only focus …

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 …

This study presents the model and control of the magnetic levitation system. The model
considers the angular position of the ball, also a neural network approximates the …

During the last 15 to 20 years, evolving (neuro-) fuzzy systems (E (N) FS) have enjoyed
more and more attraction in the context of data stream mining and modeling processes. This …