Prediction of fabric thermal-moisture comfort based on least squares support vector machines

The application of artificial intelligence and machine learning methods to textile fashion engineering facilitates the prediction of thermal-moisture comfort of fabrics.Thirty-six kinds of knitted fabrics are investigated and the relationship between the thermal-moisture comfort objective evaluation indices and the subjective wear evaluation indices of the fabrics are analyzed by least squares support vector machines(LSSVM).And regression models are created to predict the subjective evaluation using objective evaluation indices as input parameters.These models can quickly predict the subjective wear comfort when the fabrics are made into clothes and significantly reduce the cost of evaluation for the development of new fabric materials.Analytical comparison of different regression models demonstrated that the LSSVM model yields more accurate prediction of subjective thermal-moisture comfort than the BP model.