This paper presents a novel approach in learning algorithms commonly used for training radial basis function (RBF) neural networks. This approach could be used in applications that need real-time capabilities for retraining RBF neural networks. The proposed method is a Three-Phase Learning Algorithm that optimizes the functionality of the Optimum Steepest Decent (OSD) learning method. RBF neura...

| Adaptive methods for demodulating multiuser communication in a Direct-Sequence Spread-Spectrum Multiple-Access (DS/SSMA) environment are investigated. In this setting the noise is characterized as being the sum of the interfering users' signals and additive Gaussian noise. The optimal receiver for DS/SSMA systems has a complexity that is exponential in the number of users. Adaptive Radial Bas...

Random beamforming (RBF) has received much attention recently in downlink beamforming because of its simple structure, low-feedback load and same throughput scaling as that obtained using dirty paper coding at the transmitter. In this study, the authors analyse the performance of RBF for cognitive downlink multi-antenna system in terms of the throughput of the secondary network. The authors con...

The Dst index is a key parameter which characterises the disturbance of the geomagnetic field in magnetic storms. Modelling of the Dst index is thus very important for the analysis of the geomagnetic field. A data-based modelling approach, aimed at obtaining efficient models based on limited inputoutput observational data, provides a powerful tool for analysing and forecasting geomagnetic activ...

Dams have been always considered as the important infrastructures and their critical values are counted. Hence, evaluation and avoidance of dams’ destruction have a specific importance. Seepage occurrence in dams is an inevitable phenomenon. Despite all the progress in geotechnical engineering, up to now, seepage problem is the main conflict which occurs in dams. This study tried to estimate se...

Based on radial basis function neural network (RBF NN),the paper proposed a new algorithm for strip shape recognition. Compared with back propagation (BP) algorithm and improved least squares method (LSM), RBF NN shows excellent overall performance, such as learning speed, recognition precision and anti-interference capability. Copyright©2005IFAC Keyword: Strip Shape, Pattern Recognition, RBF,B...

The aim of this paper is to show the possible improvement of the reliability of classification of RBF networks using genetic algorithms for attribute selection. A disadvantage of RBF networks is that they cannot deal effectively with irrelevant features. Genetic search may filter features leading to reduce dimensionality of the feature space. In our experiments, genetic search improves classifi...

A new learning algorithm called extreme learning machine (ELM) has recently been proposed for single-hidden layer feedforward neural networks (SLFNs) with additive neurons to easily achieve good generalization performance at extremely fast learning speed. ELM randomly chooses the input weights and analytically determines the output weights of SLFNs. It is proved in theory that ELM can be extend...

A neural network model based on a chaotic particle swarm optimization (CPSO) radial basis function-back propagation (RBF-BP) was suggested to improve the accuracy of reactor temperature prediction. The training efficiency RBF-BP is influenced some degree by large randomness initial weight and threshold. To address impact threshold uncertainty combined network, this paper proposes using algorith...