摘要： 将禁忌搜索和遗传算法相结合，提出一种改进的最小二乘支持向量机(LS-SVM)参数优选方法。利用自适应遗传算法进行全局搜 索，使用禁忌搜索进行局部寻优，由此提高求解速度和解的精度。采用某冶炼厂净化工段的现场数据建立模型进行仿真实验，结果表明，该方法能使LS-SVM模型具有较好的泛化能力，模型精度满足工艺要求。

关键词: 最小二乘支持向量机, 参数优选, 遗传算法, 禁忌搜索, 预测建模

Abstract: This paper proposes an improved Least Squares Support Vector Machine(LS-SVM) parameter optimized selection method by combining Tabu Search(TS) and Genetic Algorithm(GA). Self-adaptive GA is used to search the global space and TS is used for searching the local area, so that the efficiency and precision of the solution are improved. A prediction model based on the method is established and simulated by the field data from the purification process in a smelt factory. Simulation results show that the method makes LS-SVM model have good generalization performance and high precision which can satisfy the technology requirement.

Key words: Least Squares Support Vector Machine(LS-SVM), parameter optimized selection, Genetic Algorithm(GA), Tabu Search(TS), prediction modeling

中图分类号: 

• TP391