In recent years, artificial neural networks and their applications for large data sets have become a crucial part of scientific research. this work, we implement the Multilayer Perceptron (MLP), which is class feedforward network (ANN), to predict ground-state binding energies atomic nuclei. Two different MLP architectures with three four hidden layers are used study effects on predictions. To ...

A novel improvement in neural network training for pattern classification is presented in this paper. The proposed training algorithm is inspired by the biological metaplasticity property of neurons and Shannon’s information theory. This algorithm is applicable to artificial neural networks (ANNs) in general, although here it is applied to a multilayer perceptron (MLP). During the training phas...

the objective of this paper is to develop an artificial neural network (ann) model which can beused to predict temperature rise due to climate change in regional scale. in the present work data recorded overyears 1985-2008 have been used at training and testing steps for ann model. the multilayer perceptron(mlp) network architecture is used for this purpose. three applied optimization methods a...

Abstract— This paper considers two important classification algorithms for to classify several power quality disturbances. Artificial Neural Network (ANN) and support vector machine (SVM). The last one is a novel algorithm that has shown good performance in general patterns classification. Nevertheless, Multilayer Perceptron Artificial Neural Network (MLPANN) is the most popular and most widely...

In this paper a novel technique is proposed for the estimation of resonant frequency of coaxial feed equilateral triangular microstrip patch antenna. The major advantage of the proposed approach is that, after proper training, proposed neural model completely bypasses the repeated use of complex i terative process for calculation of resonant frequency, thus resulting in an extremely fast soluti...

This paper presents a novel artificial neural network (ANN) model estimating vehicle-level radiated magnetic emissions of an electric car as a function of the corresponding driving pattern. Real world electromagnetic interference (EMI) experiments have been realized in a semi-anechoic chamber using Renault Twizy. Time-domain electromagnetic interference (TDEMI) measurement techniques have been ...

Abstract. Dry Low Emission (DLE) gas turbine has been developed as a solution to encounter the harmful high NOx emission from conventional turbine. However, it is prone create Lean Blowout (LBO) error that causes frequent shutdown due its stringent condition needs be operate inside desired operating can monitored through temperature, and CO concentration. This paper develops an Artificial Neura...

have aroused great interest in fi elds as diverse as biology, psychology, medicine, economics, mathematics, statistics and computer science. The main reason underlying this interest lies in the fact that ANN are general, fl exible, nonlinear tools capable of approximating any sort of arbitrary function (Hornik, Stinchcombe, & White, 1989). Due to their fl exibility as function approximators, AN...

-------------------------------------------------------------------ABSTRACT---------------------------------------------------------------Prediction of rainfall for a region is of utmost importance for planning, design and management of irrigation and drainage systems. This can be achieved by different approaches such as deterministic, conceptual, stochastic and Artificial Neural Network (ANN)....