Strict Interpolation of a Smooth Function and Its First Derivative Using a Linearly-Trained Radial Basis Function Neural Network

Justin Steven Prentice


We present a neural network, based on Gaussian functions, for interpolating a univariate function and its first derivative. The network is linearly trained, and constitutes a continuous piecewise approximation. It is based on the superposition of three standard Gaussian-based radial basis function networks. Analysis indicates that the network is a better approximation than the standard network.

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Journal of Mathematics Research   ISSN 1916-9795 (Print)   ISSN 1916-9809 (Online)

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