Precision coupling system modelling based on rough set and support vector machine

This paper presents the results of a research into the use of rough set (RS) for the precision coupling system modelling based on support vector machine (SVM). Because of the impact of multiple geometric parameters, there are more input variables in the precision coupling system modelling process. The high-dimensional data poses an interesting challenge to machine learning, as the presence of large numbers of redundant or highly correlated variables can seriously degrade modelling accuracy. In this study, a modelling method was developed based on rough set and support vector machine for precision coupling system. We used RS as the pre-processor for precision coupling system modelling, so as to realise dimension reduction of the high-dimensional data and improve the predictive performance of machine learning method, and then SVM was used for precision coupling system modelling. Experiments were carried out on a typical precision coupling, hydraulic valve. The results show that the use of RS method can improve the performance of machine learning in the modelling of high-dimensional data.