Quality Analysis of Polysilicon Ingot Batching Using NRS-SVM Two-stage Genetic Algorithm

Abstract

In the quality analysis of polysilicon ingot batching, a two-stage genetic algorithm (NRS-SVM-GA) combining the NRS-SVM model with genetic algorithm (GA) was proposed to solve the problem of neighborhood radius and SVM parameter values in the processing of continuous data of polysilicon ingot batting with rough set-support vector machine (NRS-SVM) model. The first stage of the algorithm, by searching for a new neighborhood radius, a better reduction set is obtained. In the second stage the attribute reduction results from the first stage are adopted to, by searching the new parameters of SVM, train the classification model with higher accuracy. According to the purpose of each stage, the corresponding fitness function and termination conditions are put forward. The distinctive features of this method is to implement the NRS-SVM automatic feature extraction and classification prediction, and run the two stages independently to avoid the time consumption for the evaluation of reduction by classifiec. The experimental results of polysilicon ingot batching data set show that this method has shorter running time, stable output, less features, and higher classification accuracy than the standard genetic algorithm.

Keywords

• neighborhood rough set-support vector machine (nrs-svm)
• genetic algorithm (ga)
• polysilicon ingot batching
• neighborhood radius
• attribute reduction
• svm parameters
• fitness function
• termination conditions
• automation