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...

ICE is a new incremental construction algorithm of a hybrid system for continuous learning tasks. The basis of the hybrid system is a radial basis function (RBF) network layer. The second layer consists of local models. The two layers are closely combined with a strong interaction. For example information from the model-layer is used by the RBF-layer to decide if new RBF-neurons are needed and ...

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...

This paper works on hybrid force/position control in robotic manipulation and proposes an improved radial basis functional RBF neural network, which is a robust relying on the Hamilton Jacobi Issacs principle of the force control loop. The method compensates uncertainties in a robot system by using the property of RBF neural network. The error approximation of neural network is regarded as an e...

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...

This paper presents a new neural network based controller design for multivariable systems. The proposed controller is designed using radial basis function (RBF) neural network. Weight update equation using classical least mean square principle is derived for the RBF network. The controller generates optimal control signals abiding by constraints, if any, on the control signals. Simulation resu...