Conventionally, in radial basis function (RBF) network width factor is constructed by obtaining r-nearest neighbor rule or taking equal to a constant for all Gaussian functions. This paper proposes an approach for the construction of width factor using genetic algorithm to optimize the Gaussian function. Our experimental results show that our proposed optimal-based width outperforms the convent...

In this article, we investigate the execution of a meshless method line (MMOL) to solve general seventh-order Korteweg-de Vries (KdV7) equations numerically. The suggested technique uses radial basis functions (RBF) for spatial derivatives and Runge-Kutta (RK) time governing equation. To produce an efficient numerical solution, three different types RBF are used. method?s output is successfully...

One commonly finds in applications of smooth radial basis functions (RBFs) that scaling the kernels so they are ‘flat’ leads to smaller discretization errors. However, the direct numerical approach for computing with flat RBFs (RBF-Direct) is severely ill-conditioned. We present an algorithm for bypassing this ill-conditioning that is based on a new method for rational approximation (RA) of vec...

Using radial basis function networks for function approximation tasks suffers from unavailable knowledge about an adequate network size. In this work, a measuring technique is proposed which can control the model complexity and is based on the correlation coefficient between two basis functions. Simulation results show good performance and, therefore, this technique can be integrated in the RBF...

Estimate of sediment load is required in a wide spectrum of water resources engineering problems. The nonlinear nature of suspended sediment load series necessitates the utilization of nonlinear methods to simulate the suspended sediment load. In this study Artificial Neural Networks (ANNs) are employed to estimate daily suspended sediment load. Two different ANN algorithms, Multi Layer Perce...

In this paper, a new localized radial basis function (RBF) method based on partition of unity (PU) is proposed for solving boundary and initial-boundary value problems. The benefits from direct discretization approach called the “direct RBF (D-RBF-PU)” method. Thanks to avoiding all derivatives PU weight functions as well lower local approximants, faster simpler than standard RBF-PU Besides, di...

This paper considers the problem of modelling a 3-D scene from calibrated images taken from multiple viewpoints. The initial 3-D information is acquired using probabilistic space carving which provides a voxel representation consistent with the given set of images. The scene is afterwards modelled as an implicit surface using radial basis functions (RBF). The mixture of multiorder basis functio...

In this paper, we propose a methodology for training a new model of artificial neural network called the generalized radial basis function (GRBF) neural network. This model is based on generalized Gaussian distribution, which parametrizes the Gaussian distribution by adding a new parameter t. The generalized radial basis function allows different radial basis functions to be represented by upda...

We review the use of feed-forward networks as estimators of probability densities in hidden Markov modelling. In this paper we are mostly concerned with radial basis functions (RBF) networks. We note the isomorphism of RBF networks to tied mixture density estimators; additionally we note that RBF networks are trained to estimate posteriors rather than the likelihoods estimated by tied mixture d...

This paper proposes a generic criterion that defines the optimum number of basis functions for radial basis function (RBF) neural networks. The generalization performance of an RBF network relates to its prediction capability on independent test data. This performance gives a measure of the quality of the chosen model. An RBF network with an overly restricted basis gives poor predictions on new...