Accurate estimation of parameters during transient and steady state is required for controlling of Induction motor. Artificial neural networks (ANNs) based online identification of induction motor parameters are presented. ANNs such as feed forward network is used to develop an ANN as a memory for remembering the estimated parameters and for computing the parameters during transients. Simulatio...

Adaptation during evolution has been an important focus of research in training neural networks. Cooperative coevolution has played a significant role in improving standard evolution of neural networks by organizing the training problem into modules and independently solving them. The number of modules required to represent a neural network is critical to the success of evolution. This paper pr...

This paper presents a neural network approach in modeling of torque estimation and Parks d–q transformation for an open-loop induction machine. The nonlinear approximation capability of neural networks makes it possible to map the Parks d–q transformation and torque estimation in an induction motor, which would otherwise require extensive complex calculations. The neural network simulation resu...

Artificial Neural Networks have achieved satisfactory results in different fields such as example classification or image identification. Real-world processes usually have a temporal evolution, and they are the type of processes where Recurrent Networks have special success. Nevertheless they are still reluctantly used, mainly due to the fact that they do not adequately justify their response. ...

Neural networks have been successfully applied in a wide range of supervised and unsuper vised learning applications Neural network methods are not commonly used for data mining tasks however because they often produce incomprehensible models and require long training times In this article we describe neural network learning algorithms that are able to produce comprehensible models and that do ...

The paper focuses on the problem of rule extraction from neural networks, with the aim of transforming the knowledge captured in a trained neural network into a familiar form for human user. The ultimate purpose for us is to develop human friendly shells for neural network based systems. In the first part of the paper it is presented an approach on extracting traditional crisp rules out of the ...

Abstruct Induction motors are exited machines used in a lot of areas in industry because o f the features like the simpleness of the structure, the easiness of the control and the necessity of the fewer care. One of priority reasons of induction motors is that the speed adjusting can easily be done by wide ranges by means of the developing of solid-state technology. In this paper, it is put for...

Many different discrete-time recurrent neural network architectures have been proposed. However, there has been virtually no effort to compare these arch:tectures experimentally. In this paper we review and categorize many of these architectures and compare how they perform on various classes of simple problems including grammatical inference and nonlinear system identification.