Abstract: Support vector machine regression is an important machine learning algorithm,and it has been applied to some areas successfully.However,single-output support vector regression (SVR) has long training time and lacks practicality in some complex systems.The multi-output intuitionistic fuzzy least squares support vector regression (IFLS-SVR) introduces intuitionistic fuzzy on the basis of multi-output SVR to solve the problem of uncertain multi-output complex system and reduce the training time.Most applications in life are complex.Based on traditional support vector regression,this paper proposed a multi-output intuitionistic fuzzy least squares support vector regression model (IFLS-SVR).The multi-output IFLS-SVR transforms the actual data into fuzzy data by intuitionistic fuzzy algorithm,and transforms the quadratic programming optimization problem into the process of solving a series of linear equations.Compared with the existing fuzzy support vector regression,the multi-output IFLS-SVR uses the intuitionistic fuzzy method to calculate the membership function,and exploits least square method to improve the training efficiency,thus reducing the training time,obtaining more accurate solution.Compared with other methods,the multi-output IFLS-SVR achieves good results by simulation model.Finally,the multi-output IFLS-SVR model also performs excellently when it is applied to predict the wind speed and wind direction.

Key words: Intuitionistic fuzzy, Least squares support vector regression, Multi-output, Wind speed and wind direction prediction