# Approximation of function and its derivatives using radial basis function networks

Mai-Duy, Nam and Tran-Cong, Thanh (2003) Approximation of function and its derivatives using radial basis function networks. Applied Mathematical Modelling, 27 (3). pp. 197-220. ISSN 0307-904X  Preview
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## Abstract

This paper presents a numerical approach, based on radial basis function networks (RBFNs), for the approximation of a function and its derivatives (scattered data interpolation). The approach proposed here is called the indirect radial basis function network (IRBFN) approximation which is compared with the usual direct approach. In the direct method (DRBFN) the closed form RBFN approximating function is first obtained from a set of training points and the derivative functions are then calculated directly by differentiating such closed form RBFN. In the indirect method (IRBFN) the formulation of the problem starts with the decomposition of the derivative of the function into RBFs. The derivative expression is then integrated to yield an expression for the original function, which is then solved via the general linear least squares principle, given an appropriate set of discrete data points. The IRBFN method allows the filtering of noise arisen from the interpolation of the original function from a discrete set of data points and produces a greatly improved approximation of its derivatives. In both cases the input data consists of a set of unstructured discrete data points (function values), which eliminates the need for a discretisation of the domain into a number of finite elements. The results obtained are compared with those obtained by the feed forward neural network approach where appropriate and the 'finite element' methods. In all examples considered, the IRBFN approach yields a superior accuracy. For example, all partial derivatives up to second order of the function of three variables y=x12+x1x2−2x22−x2x3+x32 are approximated with at least an order of magnitude better in the L2-norm in comparison with the usual DRBFN approach. Statistics for this ePrint Item
Item Type: Article (Commonwealth Reporting Category C) Yes Live Archive © 2002 Elsevier Science Inc. Author's version deposited in accordance with the copyright policy of the publisher. Historic - Faculty of Engineering and Surveying - Department of Mechanical and Mechatronic Engineering (Up to 30 Jun 2013) Historic - Faculty of Engineering and Surveying - Department of Mechanical and Mechatronic Engineering (Up to 30 Jun 2013) 11 Oct 2007 01:14 09 Sep 2013 06:21 radial basis function networks; function approximation; derivative approximation; scattered data interpolation; global approximation 01 Mathematical Sciences > 0101 Pure Mathematics > 010111 Real and Complex Functions (incl. Several Variables)01 Mathematical Sciences > 0102 Applied Mathematics > 010201 Approximation Theory and Asymptotic Methods E Expanding Knowledge > 97 Expanding Knowledge > 970101 Expanding Knowledge in the Mathematical Sciences https://doi.org/10.1016/S0307-904X(02)00101-4 http://eprints.usq.edu.au/id/eprint/2780

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