Feature extraction of spinning-production process decision parameters based on Isomap
The spinning production involves many process parameters,which are interrelated and redundant.Since it is diffi-cult to accurately predict and control the quality indicators caused by the correlation between the process parameters,in this arti-cle a feature-extraction method is proposed based on Isometric Mapping(Isomap)for the spinning-production process decision pa-rameters.Firstly,the grey correlation analysis is used to rank the grey correlation between the process parameters and the yarn quality indicators;efforts are made to select the process parameters that have a significant impact on the produc t quality indicators as the decision parameters.Furthermore,these decision parameters are used for feature extraction and dimensionality reduction by means of Isomap;efforts are made to obtain the independent low-dimensional decision-parameter feature spaces and input them in-to the Particle Swarm Optimization Vector Machine Model(PSO-SVM),so as to verify this method's feature-extraction perform-ance.Finally,the numerical examples are used to verify the results;it is shown that this method has better prediction perform-ance by utilizing fewer feature space dimensions compared to the original data.
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