This work analyzes the advantages of Artificial Neural Networks applied to sensorless control for Switched Reluctance Motors. Due to the non-linear electrical characteristics of Switched Reluctance Motors, Artificial Neural Networks perform a good tool and for torque ripple minimization in Switched Reluctance Motors. The simulation results to prove the efficiency of a multilayer perceptron in o...

In this contribution is investigated the use of multilayer perceptron type neural networks in the characterization of images by texture content. The paper is focused on the effects of textural feature extraction methods on the network architecture, training performance and generalization capability when applied in indexing of images contained within multimedia image databases. An in depth exper...

This paper presents a method of recognition of isolated offline handwritten Devanagari numerals using wavelets and neural network classifier. This method of optical character recognition takes the handwritten numeral image as input. After pre-processing, it is subjected to single level wavelet decomposition using Daubechies-4 wavelet filter. This wavelet decomposition allows viewing the input n...

An implementation of an optical perceptron with a soft optical threshold trained with an adapted BP algorithm is described as a precursor to an optical multilayer perceptron (MLP). It has 64 inputs and ten outputs. The soft threshold is implemented by a liquid crystal light valve. Experimental results on perceptron recall are also reported. The eeect of a modiied grey-scale to weight mapping fo...

Several neural network architectures have been developed over the past several years. One of the most popular and most powerful architectures is the multilayer perceptron. This architecture will be described in detail and recent advances in training of the multilayer perceptron will be presented. Multilayer perceptrons are trained using various techniques. For years the most used training metho...

This paper describes an experiment where classifiers are used to identify potential diagnoses on examining textual content of medical records. Three classifiers are applied separately (k-nearest neighborhood, multilayer perceptron and support vector machines) and also combined in two different approaches (parallel and cascading); results show that even accuracy point to a best alternative, ROC ...

The training algorithm studied in this paper is inspired by the biological metaplasticity property of neurons. Tested on different multidisciplinary applications, it achieves a more efficient training and improves Artificial Neural Network Performance. The algorithm has been recently proposed for Artificial Neural Networks in general, although for the purpose of discussing its biological plausi...

Removal of boilerplate is among the essential tasks in web corpus construction and web indexing. In this paper, we present an improved machine learning approach to general-purpose boilerplate detection for languages based on (extended) Latin alphabets (easily adaptable to other scripts). We keep it highly efficient (around 320 documents per single CPU core second) by using an optimized Multilay...