Basic definitions concerning the multi-layer feed-forward neural networks are given. The back-propagation training algorithm is explained. Partial derivatives of the objective function with respect to the weight and threshold coefficients are derived. These derivatives are valuable for an adaptation process of the considered neural network. Training and generalisation of multi-layer feed-forwar...

Optimizing antenna arrays to approximate desired far field radiation patterns is of exceptional interest in smart antenna technology. This paper shows how to apply artificial intelligence, in the form of neural networks, to achieve specific beam-forming with linear antenna arrays. Multilayer feed-forward neural networks are used to maximize multiple main beams’ radiation of a linear antenna arr...

The estimation and forecasting of the hydrologic data carry significance for many water resources engineering problems. Establishing sediment monitoring instruments on rivers is a costly operation. The methods available in literature for sediment concentration estimation are complicated, time consuming and necessitate cumbersome parameter estimation procedures. Artificial neural networks have b...

Neural networks are a powerful tool used to model properties and behaviour of materials in many areas of civil engineering applications. In the present paper, the models in artificial neural networks for predicting the temperatures in timber under fire loading have been developed. For building these models, training and testing using the available numerical results obtained using design methods...

Image processing is simply the processing of the given image. The input is just an image, that may be from any source, and the output may be an image or a set of parameters that are related to that particular image. Recognition plays an important role in the area of image processing. In this research work, the authors focus on the recognition of handwritten digits. A new method that uses neural...

This paper demonstrates classification of PQ events utilizing wavelet transform (WT) energy features by artificial neural network (ANN) and SVM classifiers. The proposed scheme utilizes wavelet based feature extraction to be used for the artificial neural networks in the classification. Six different PQ events are considered in this study. Three types of neural network classifiers such as feed ...

Neural network techniques for encodlng-decoding processes have been developed. The net we have devised can work like it memory retrieval system in the sense of Hopfield, Feinstein and Palmex. Its behaviour for 2 R (R E N) input units has some special interesting features. In particular, the accsssibilities for each initial symbol may be explicitly computed. Although thermal noise may muddle the...

This paper is dedicated to the long-term, or multi-step-ahead, time series prediction problem. We propose a novel method for training feed-forward neural networks, such as multilayer perceptrons, with tapped delay lines. Special batch calculation of derivatives called Forecasted Propagation Through Time and batch modification of the Extended Kalman Filter are introduced. Experiments were carrie...