estimation of the metal value in the metallic deposits is one of the important factors to evaluate the deposits in exploration studies andmineral processing. therefore, one accurate estimator is essential to obtain a fine insight into the accumulation of the ore body. thereare geostatistical methods for the estimation of the concentration of iron which performance of these models is complexity ...

Abstract— In a reverberant environment, the performance of acoustic event recognition system can be bolstered by choosing appropriate feature descriptors and classifier techniques. Neural networks are by far providing stunning classification results when compared to other classifiers. This paper analyses two different neural networks and their precision when they both stumble upon same targets ...

In order to further improve the precision and generalization ability of the neural network based performance model of engine, back propagation neural network (BPNN), radial basis function neural network (RBFNN) and generalized regression neural network (GRNN) have been investigated. The topologies and algorithms of these three different types of neural networks have been designed to meet the sa...

Artificial Neural Networks (ANN) trained using backpropagation (BP) algorithm are commonly used for modeling material behavior associated with non-linear, complex or unknown interactions among the material constituents. Despite multidisciplinary applications of back-propagation neural networks (BPNN), the BP algorithm possesses the inherent drawback of getting trapped in local minima and slowly...

-There have been a number of methods presented by various researchers for traffic prediction, some of which involve modeling the problem of traffic prediction as a time series. It has been observed that Artificial Neural Networks (ANN) perform better than statistical methods for time series forecasting. The network performance and complexity varies with the choice of algorithm used. Back propag...

In this paper, series of experimental studies about the neural network model for whiskbroom remote sensing imagery geometry correction methods based on BPNN (Back-Propagation Neural Networks) and RBFNN (Radial Basis Functions Neural Networks) with detailed algorithm were raised initially, which were presented on the focus of the establishment of the neural network model for geometric correction...

The paper presents research on the problem of recognition of signs applied to the analysis of basic units of ancient Russian chants. For testing, we take two types of deep neural networks: Back Propagation Neural Network (BPNN) and Convolution Neural Network (CNN). We investigate main features of the chant units and the properties of the networks to choose the best structure and algorithm. The ...

Natural outdoor scene classification is active and promising research area around the globe. In this study, the classification is carried out in two phases. In the first phase, the features are extracted from the images by wavelet decomposition method and stored in a database as feature vectors. In the second phase, the neural classifiers such as back-propagation neural network (BPNN) and resil...

Various artificial neural networks types are examined and compared for the prediction of surface roughness in manufacturing technology. The aim of the study is to evaluate different kinds of neural networks and observe their performance and applicability on the same problem. More specifically, feed-forward artificial neural networks are trained with three different back propagation algorithms, ...

Metaheuristic algorithm such as BAT algorithm is becoming a popular method in solving many hard optimization problems. This paper investigates the use of Bat algorithm in combination with Back-propagation neural network (BPNN) algorithm to solve the local minima problem in gradient descent trajectory and to increase the convergence rate. The performance of the proposed Bat based Back-Propagatio...