Scaling properties of neural networks, that are the relations between the number of hidden units and the training or generalization error, recently have been investigated theoretically with encouraging results. In our paper we investigate experimentally, whether the theoretic results may be expected in practical applications. We investigate different neural network structures with varying numbe...

Intelligent systems are yielded from integration of a logic programming and connectionist systems. Radial basis function neural network is a commonly-used type of feedforward networks. In this paper, we proposed a method for connectionist model generation using Radial Basis Function neural network to encode higher order logic programming. We encode each clause from the clauses of a logic progra...

A modelling of robot manipulator dynamics by means of a neural architecture is presented. Such model is applicable to generate a decoupling and linearising feedback in the control system of the robot. In a structured model approach, a RBF-like neural network is used to represent and adapt all model parameters with their dependences on the joint positions. The neural network is hierarchically or...

This paper presents a comparative study of the hybrid models, neural networks and nonparametric regression models in time series forecasting. The components of these hybrid models are consisting of the nonparametric regression and artificial neural networks models. Smoothing spline, regression spline and additive regression models are considered as the nonparametric regression components. Furth...

The goal of the paper is to present a speech nonfluency detection method based on linear prediction coefficients obtained by using the covariance method. The application “Dabar” was created for research. It implements three different methods of LP with the ability to send coefficients computed by them into the input of Kohonen networks. Neural networks were used to classify utterances in catego...

In view of disasters caused by rock burst becoming more and more serious in coal mine production, three models are established for evaluation and prediction the rock burst risk based on artificial neural network. First, ten indicators are determined which have a larger influence on rock burst. Then two back propagation network models are trained using the original data and the processed data re...

The work deals with development and application of Radial basis functions neural networks (RBFNN) for spatial predictions. Geostatistical tools for spatial correlation analysis (variography) are used to qualify and quantify the estimation results. Geostatistical analysis is performed on the residuals obtained at the training and test sample locations. Variogram of residuals explores spatial cor...

Handoff algorithm is used in wireless cellular systems to decide when and to which base station (BS) to handoff in order that the services can be continued uninterrupted. In this paper, we propose a handoff algorithm based on the neural network in a joint system of terrestrial and high altitude platform station (HAPS) cellular systems. As a revolutionary wireless system, HAPS can supply service...

Dimension-reducing feature extraction neural network techniques which also preserve neighbourhood relationships in data have traditionally been the exclusive domain of Kohonen self organising maps. Recently, we introduced a novel dimension-reducing feature extraction process, which is also topographic, based upon a Radial Basis Function architecture. It has been observed that the gener-alisatio...

Reservoir characterization and asset management require comprehensive information about formation fluids. In fact, it is not possible to find accurate solutions to many petroleum engineering problems without having accurate pressure-volume-temperature (PVT) data. Traditionally, fluid information has been obtained by capturing samples and then by measuring the PVT properties in a laboratory. In ...