计算机科学 ›› 2019, Vol. 46 ›› Issue (5): 163-168.doi: 10.11896/j.issn.1002-137X.2019.05.025
王定成1, 陆一祎1, 邹勇杰2
WANG Ding-cheng1, LU Yi-yi1, ZOU Yong-jie2
摘要: 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。
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