A Rational Initialization of the Feed-Forward Neural Network Regression.

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In this research, a method to derive a rational initial estimate of the parameters of neural network regression model is derived.Let the first layer of the 3-layer feed-forward neural network regression model be donated as X and the parameter (intercept and weight) matrices from the first layer to the second be θィイD12*ィエD1, WィイD12*ィエD1. Our method first define the values of the parameters so that the second layer output OィイD12*ィエD1 = ψ(1θィイD12*'ィエD1 + XWィイD12+ィエD1) (1) consists of a set of the monotone functions rich enough to cover the criterion space, Y. Then, a variable selection technique will be used to select the best fitting subset of the columns of OィイD12*ィエD1 matrix resulting in the (selected) subset OィイD12ィエD1 and the weight matrix θィイD13ィエD1, WィイD13ィエD1 using the following multivariate linear regression : Y 【approximately equal】 1θィイD13'ィエD1 + OィイD12ィエD1WィイD13ィエD1 (2)