This paper presents an alternative approach using the differential logic associated to Artificial Neural Networks (ANNs) in order to distinguish between inrush currents and internal faults in the protection of power transformers. The Alternative Transients Program (ATP) has been chosen as the computational tool to simulate a power transformer under fault and energization situations. The Radius ...

The main aim of this paper is to establish a reliable model of a process behavior both for the steady-state and unsteady-state regimes. The use of this accurate model allows distinguishing a normal mode from an abnormal one. Therefore the neural black-box identification by means of a NARX (Nonlinear Auto-Regressive with eXogenous) model has been chosen. It shows the choice and the performance o...

Attribute selection also called as feature selection is a preprocessing technique to select a set of features or subset of features from the available large collection of features. An artificial neural network is the simulation of a human brain which learns with experience. Efficiency of a model or a system in terms of cost, time and accuracy will greatly improve if proper features of a system ...

A hierarchical neural network model for the identiication of arbitrary contour shapes is presented. Tolerance towards translation, rotation and scaling is achieved far more cost-eeectively than for a fully connected multi-layer perceptron.

This work describes the neural network technique to solve location management problem. A multilayer neural model is designed to predict the future prediction of the subscriber based on the past predicted information of the subscriber. In this research work, a prediction based location management scheme is proposed for locating a mobile terminal in a communication without losing quality maintain...

In this work, a simplified Artificial Neural Network (ANN) based approach for recognition of various objects is explored using multiple features. The objective is to configure and train an ANN to be capable of recognizing an object using a feature set formed by Principal Component Analysis (PCA), Frequency Domain and Discrete Cosine Transform (DCT) components. The idea is to use these varied co...

Estimate of sediment load is required in a wide spectrum of water resources engineering problems. The nonlinear nature of suspended sediment load series necessitates the utilization of nonlinear methods to simulate the suspended sediment load. In this study Artificial Neural Networks (ANNs) are employed to estimate daily suspended sediment load. Two different ANN algorithms, Multi Layer Perce...

In this study, an artificial neural network was used to predict the minimum force required to single point incremental forming (SPIF) of thin sheets of Aluminium AA3003-O and calamine brass Cu67Zn33 alloy. Accordingly, the parameters for processing, i.e., step depth, the feed rate of the tool, spindle speed, wall angle, thickness of metal sheets and type of material were selected as input and t...

To analyze large sets of digital micrographs from high-throughput screening studies with constant accuracy, advanced image processing algorithms are necessary. In the literature, systems have been proposed applying modelbased fitting algorithms, morphological operators and artificial neural networks (ANN). Because single approaches show limited performance, we propose a hybrid system that combi...