Approximation of scattered geometric data is often a task in many engineering problems. The Radial Basis Function (RBF) approximation is appropriate for large scattered (unordered) datasets in d-dimensional space. This method is useful for a higher dimension d ≥ 2, because the other methods require a conversion of a scattered dataset to a semiregular mesh using some tessellation techniques, whi...

In this paper, a neural sliding mode control approach is developed to adjust the gain using radial basis function (RBF) network (NN) for tracking of Microelectromechanical Systems (MEMS) triaxial vibratory gyroscope. First with fixed proposed assure asymptotic stability closed loop system. Then RBF derived gradient method in switching law. With adaptive learning network, chattering phenomenon e...

one of the most important problems in geodesy is the unification of height datum. generally in geodesy; there are two types of height systems, the geometrical height based on ellipsoid and the physical height based on gravity-defined surface (zhang et al, 2009).local height datum is determined according to mean sea level (msl). in regarding to mismatch of mean sea level and geoid, on the one ha...

Function approximation has been found in many applications. The radial basis function (RBF) network is one approach which has shown a great promise in this sort of problems because of its faster learning capacity. A traditional RBF network takes Gaussian functions as its basis functions and adopts the least-squares criterion as the objective function, However, it still suffers from two major pr...

In this paper, the development and application of radial basis function-finite difference (RBF-FD) method RBF-finite time domain (RBF-FDTD) for solving electrical transient problems in power systems that are defined by time-dependent ordinary differential equations (ODEs) partial (PDEs), respectively, presented. RBFs such as Gaussian (GA), Multiquadric (MQ), Inverse Quadric (IQ), (IMQ) used the...

Studies convergence properties of radial basis function (RBF) networks for a large class of basis functions, and reviews the methods and results related to this topic. The authors obtain the network parameters through empirical risk minimization. The authors show the optimal nets to be consistent in the problem of nonlinear function approximation and in nonparametric classification. For the cla...

A meshfree approximation scheme based on the radial basis function methods is presented for the numerical solution of the options pricing model. This thesis deals with the valuation of the European, Barrier, Asian, American options of a single asset and American options of multi assets. The option prices are modeled by the Black-Scholes equation. The θ-method is used to discretize the equation ...

Radial Basis Function (RBF) neuron network is being applied widely in multivariate function regression. However, selection of neuron number for hidden layer and definition of suitable centre in order to produce a good regression network are still open problems which have been researched by many people. This article proposes to apply grid equally space nodes as the centre of hidden layer. Then, ...

This paper proposes to incorporate full covariance matrices into the radial basis function (RBF) networks and to use the Expectation-Maximization (EM) algorithm to estimate the basis function parameters. The resulting networks, referred to as elliptical basis function (EBF) networks, are evaluated through a series of text-independent speaker veriication experiments involving 258 speakers from a...

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...