A review on soft computing techniques used in induction motor drive application

Abstract

This study reviews hybrid models built using fuzzy systems and neural networks. Expertise for induction motor drives, using the learning capacity of artificial neural networks, is an explicit representation of a fuzzy inference system. The effectiveness of neuro-fuzzy approaches for training and inference in induction motor drives has drawn the attention of researchers. This article gives an overview of several artificial neural network approaches, fuzzy, type-1 fuzz logic, type-2 fuzzy logic, neuro-fuzzy systems, type-1 neuro-fuzzy and type-2 neuro fuzzy systems in accordance with the classification of research articles. The major goal is to give a succinct summary of current neuro-fuzzy research so that readers can choose appropriate strategies based on their own research interests, such as various types of neuro fuzzy systems, to enhance system performance in general.