New approach to selection of initial values of weights in neural function approximation

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Abstract
It is proven that the weights and biases generated with certain constraints based on the piecewise linear principle result in an initial neural network which is better able to form a function approximation of an arbitrary function. Use of these initial constraints greatly shortens the training time and avoids the local minima usually associated with an arbitrary random choice of initial weights.

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