Selection and Evaluation of Input Parameters of Neural Networks Using Grey Superior Analysis

Abstract

Grey superior analysis (GS) has been primarily studied in order to select the efficient input variables of an artificial neural network during the worsted yarn manufacturing. The analysis of the processed data indicates that the parameter selection by means of the grey relevancy matrix can obtain the corresponding sequence according to their correlation degree and derive out a group of main factors as the input variables with high grey-correlation for the ANN model. Through the actual calculation and prediction accuracy analysis, the parameters selected by using the grey superior analysis are more correct and effective but less in number than those produced by the subjective and empirical method popularly used in the field of the textile industry. The prediction and the optimization of the processing techniques can be executed with the ANN model optimized by GS. Moreover, three coefficients for the evaluation of the validity of the input variables selected to ANN have been put forward, which are covering experience coefficient, α, experience redundant coefficient, β, and new information coefficient, γ. Comparing the subjective and experiential method (SE) with GS, α = 0.30~0.60, β = 1.00~ 2.75, and γ = 0.17~0.35, that means the traditional used SE method is only partial correct with a relatively high redundancy.

Keywords

grey superior analysis neural network parameter selection optimization

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