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 …

As a typical example of large and complex mechanical systems, rotating machinery is prone
to diversified sorts of mechanical faults. Among these faults, one of the prominent causes of …

Despite the importance of fault diagnosis in refrigeration systems, the performance and
improvement of most existing diagnostic models are limited by their reliance on a single …

Due to its complex structure, production form a shale-gas formation requires more drillings
than those for the traditional reservoirs. Modeling of such reservoirs and making predictions …

Fault Detection and Diagnosis of electrical machine and drive systems are of utmost
importance in modern industrial automation. The widespread use of Machine Learning …

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 …

Gradual drifts in data streams are usually hard to detect and often do not necessarily trigger
the evolution of new fuzzy rules during model adaptation steps in order to represent the new …

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 …

Prognosis of discrete component with intermittent fault in hybrid systems is challenging since
the component has only two states (ie, ON and OFF) and no associated physical parameter …

The ability of evolutionary algorithms (EAs) to manage a set of solutions, even attending
multiple objectives, as well as their ability to optimize any kinds of values, allows them to fit …