In this paper a novel neural network (NN) backstepping controller is modified for application to an industrial motor drive system. A control system structure and NN tuning algorithms are presented that are shown to guarantee stability and performance of the closed-loop system. The NN backstepping controller is implemented on an actual motor drive system using a two-PC control system developed a...

A number of different methods combining the use of neural networks and genetic algorithms have been described [1]. This paper discusses an approach for training neural networks based on the geometric representation of the network. In doing so, the genetic algorithm becomes applicable as a common training method for a number of machine learning algorithms that can be similarly represented. The e...

This paper provides a short overview of neural networks (NNs) in real world industrial applications. We rst outline some of the most important features of NNs a ecting for the developing of industrial applications. We also provide lists of application areas and industrial sectors of neural networks within the Europe. Finally, some real world NN applications are reviewed, with discussion of the ...

This paper shows the results of the analysis of a scheme for Sensor Failure, Detection, Identification and Accommodation (SFDIA) using experimental flight data of a research aircraft model. Conventional approaches to the problem are based on observers and Kalman Filters while more recent methods are based on neural approximators. The work described in this paper is based on the use of neural ne...

This paper constructs fully parallel NN hardware realization of Artificial Neural Network (ANN) depends on the efficient execution of single neuron. Field Programmable Gate Array (FPGA) reconfigurable computing architecture is appropriated for hardware achievement of ANN. Numerous implementation of ANNs have been reported in scientific documents, trying to reduce Neural Networks NNs hardware ci...

The paper presents a super-twisting sliding mode (STSM) regulator with neural networks (NN) of direct power command (DPC) for controlling the active/reactive power of a doubly-fed induction generator (DFIG) using a two-level space vector pulse width modulation (2L-SVPWM). Traditional DPC strategy with proportional-integral (PI) controllers (DPC-PI) has significantly more active/reactive power r...

Neural networks (NN) have been shown to be accurate classifiers in many domains. Unfortunately, the lack of NN’s explanatory capability of knowledge learned has somewhat limited their application. A stream of research has therefore developed focusing on knowledge extraction from within neural networks. The literature, unfortunately, lacks consensus on how best to extract knowledge from help neu...

This paper deals with the predictive compression of images using neural networks (NN). The idea is to use of the backpropagation algorithm in order to compute the predicted pixels. The results validation is performed by comparison with linear prediction compression used in JPEG algorithm. Key-Words: lossless image compression, neural networks, prediction, backpropagation algorithm