Industrial Intelligent Forecast of TFT-LCD Based on R-SVM

摘要

TFT-LCD is a kind of thin film transistor liquid crystal display. The sampling test method is used to estimate the quality of the whole sample, but this method is not comprehensive and has no timeliness. The author hope that machine learning can be used to make a reasonable prediction of product quality through each process data. This paper mainly adopts the combination of SVM and random forest to form a new method: random SVM(R-SVM). Multiple SVM models were established by random sampling and number of features, and the predicted values of multiple models were averaged to obtain the final results. The evaluation standard is the mean square error(MSE). Experimental results show that R-SVM is better than traditional machine learning algorithms. As is known to as all the random forest performs best in the traditional machine learning algorithm. The MSE of our experimental results is 0.6 percentage lower than that of the random forest. The research method of this paper have brought new research ideas for industrial data prediction for the future. It also provides an opportunity for the combination of random forest and other traditional algorithms.