In this paper, a novel hybrid Radial Basis Function Neural Network (RBF–NN) ensemble model is proposed for rainfall forecasting based on Kernel Partial Least Squares Regression (K–PLSR). In the process of ensemble modeling, the first stage the initial data set is divided into different training sets by used Bagging and Boosting technology. In the second stage, these training sets are input to t...

in this article, we apply the multiquadric radial basis function (rbf) interpo-lation method for nding the numerical approximation of traveling wave solu-tions of the kawahara equation. the scheme is based on the crank-nicolsonformulation for space derivative. the performance of the method is shown innumerical examples.

Based on the radial basis functions (RBF) and T-Trefftz solution, this paper presents a new meshless method for numerically solving various partial differential equation systems. First, the analog equation method (AEM) is used to convert the original patial differential equation to an equivalent Poisson’s equation. Then, the radial basis functions (RBF) are employed to approxiamate the inhomoge...

| Although our previous model CLARION has shown some measure of success in reactive sequential decision making tasks by utilizing a hybrid architecture which uses both procedural and declarative learning, it suuers from a number of problems because of its use of back propagation networks. CLARION-RBF is a more parsimonious architecture that remedies some of the problems exhibited in CLARION by ...

The method presented here is aimed to a direct fast setting of the parameters of a RBF network for function approximation. It is based on a hierarchical gridding of the input space; additional layers of Gaussians at lower scales are added where the residual error is higher. The number of the Gaussians of each layer and their variance are computed from considerations grounded in the linear filte...

in this paper, a technique generally known as meshless numerical scheme for solving fractional dierential equations isconsidered. we approximate the exact solution by use of radial basis function(rbf) collocation method. this techniqueplays an important role to reduce a fractional dierential equation to a system of equations. the numerical results demonstrate the accuracy and ability of this me...

In this paper, we proposed a method to design a model-following adaptive controller for linear/nonlinear plants. Radial basis function neural networks (RBF-NNs), which are known for their stable learning capability and fast training, are used to identify linear/nonlinear plants. Simulation results show that the proposed method is effective in controlling both linear and nonlinear plants with di...

In this article, we apply the Multiquadric radial basis function (RBF) interpo-lation method for nding the numerical approximation of traveling wave solu-tions of the Kawahara equation. The scheme is based on the Crank-Nicolsonformulation for space derivative. The performance of the method is shown innumerical examples.

In this paper, a technique generally known as meshless numerical scheme for solving fractional dierential equations isconsidered. We approximate the exact solution by use of Radial Basis Function(RBF) collocation method. This techniqueplays an important role to reduce a fractional dierential equation to a system of equations. The numerical results demonstrate the accuracy and ability of this me...