Based on the radial basis function (RBF), nonsingular general solution, and dual reciprocity method (DRM), this paper presents an inherently meshless, integration-free, boundaryonly RBF collocation technique for numerical solution of various partial differential equation systems. The basic ideas behind this methodology are very mathematically simple. In this study, the RBFs are employed to appr...

Two difficulties are involved with traditional RBF networks: the initial configuration of an RBF network needs to be determined by a trial-and-error method, and the performance suffers degradation when the desired locations of the center of the RBF are not suitable. A novel RBF network is proposed to overcome these difficulties. A new radial basis function is used for hidden nodes, and the numb...

This paper proposes a neural net based on-line scheme for modelling discrete-time mul-tivariable nonlinear dynamical systems. Taking the advantage of structural features of RBF (Radial-Basis-Function) neural nets, the method approaches the modelling problem by setting up a coarse RBF model structure in the light of the spatial Fourier transform and spatial sampling theory, then devising appropr...

In this paper, two feed forward neural network models have been presented to predict the Silicon Modification Level (SiML) of W319 aluminum alloys using the Thermal Analysis (T.A) parameters as inputs. The developed neural networks are a Multilayer Perceptron (MLP) network and a Radial Basis Function (RBF) network. The neural network models were found to predict the SiML accurately (R=0.99). Th...

This paper proposes a new blind watermarking scheme based on discrete wavelet transform(DWT) domain. The method uses the HVS model, and radial basis function neural networks(RBF). RBF will be implemented while embedding and extracting watermark.The human visual system (HVS) model is used to determine the watermark insertion strength. The neural networks almost exactly recover the watermarking s...

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...

Promising numerical results using once and twice integrated radial basis functions have been recently presented. In this work we investigate the integrated radial basis function (IRBF) concept in greater detail, connect to the existing RBF theory, and make conjectures about the properties of IRBF approximation methods. The IRBF methods are used to solve PDEs. c © 2006 Elsevier Science Ltd. All ...

In this work an attempt has been made to estimate the pollution flashover voltage under various meteorological factors using radial basis function (RBF) neural networks. Orthogonal least squares (OLS) learning method is used in order to improve the lines performance against the pollution flashover of the post insulators. The technique of RBF neural network is employed to model the relationship ...

We propose a simple but efficient method to extract rules from the radial basis function (RBF) neural network. Firstly, the data are classified by an RBF classifier. During training the RBF network, we allow for large overlaps between clusters corresponding to the same class to reduce the number of hidden neurons while maintaining classification accuracy. Secondly, centers of the kernel functio...

Radial Basis Function (RBF) methods that employ infinitely differentiable basis functions featuring a shape parameter are theoretically spectrally accurate methods for scattered data interpolation and for solving Partial Differential Equations. It is also theoretically known that RBF methods are most accurate when the linear systems associated with the methods are extremely ill-conditioned. Thi...