A method for approximating one-dimensional functions
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Abstract A nontraditional approach is presented to the approximation of a one-dimensional function defined on a discrete set of points. The method is based on the application of an artificial feedforward neural network, which realizes expansion of the function considered in orthogonal Chebyshev polynomials and which calculates the expansion coefficients during the network training.
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