多输出直觉模糊最小二乘支持向量回归算法

Multi-output Intuitionistic Fuzzy Least Squares Support Vector Regression Algorithm

支持向量机回归是一种重要的机器学习算法,虽然已成功应用于多个领域,但针对复杂系统,单输出支持向量回归算法的训练时间过长并且缺乏实用性。多输出直觉模糊最小二乘支持向量回归(Intuitionistic Fuzzy Least Squares Support Vector Regression,IFLS-SVR)在多输出支持向量机的基础上引入了直觉模糊,解决了不确定多输出复杂系统问题,减少了训练时间。生活中复杂的多输出模型更为常见,文中在传统支持向量回归的基础上对其进行改进,提出多输出IFLS-SVR模型。多输出IFLS-SVR采用直觉模糊算法将实际数据转化为模糊数据,将二次规划优化问题转化为求解一系列线性方程组。与现有的模糊支持向量回归相比,多输出IFLS-SVR采用直觉模糊方法来计算隶属度函数,采用最小二乘法提高了算法的训练效率,减少了训练时间,获得了更精确的解。仿真结果表明,与其他方法相比,多输出IFLS-SVR取得了较好的效果。最后将多输出IFLS-SVR模型应用于复杂的风速风向预测,也取得了较好的效果。

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.