参考文献 (References) 杨大锦, 朱华山, 陈加希. 湿法提锌工艺与技术[M]. 北京: 冶金工业出版社, 2006. (YANG D J, ZHU H S, CHEN J X. Zinc hydrometallurgy process and technology[M]. Beijing: Metallurgy Industry Press, 2006.) [2]杨辉, 谭明皓, 柴天佑. 基于神经网络的多元稀土萃取组分含量的软测量[J]. 中国稀土学报, 2003, 21(4): 425-430. (YANG H, TAN M H, CHAI T Y. Neural networks based component content soft-sensor in countercurrent rare-earth extraction[J]. Journal of Chinese Rare Earth Society, 2003, 21(4): 425-430.) [3]贾润达, 毛志忠, 常玉清, 等. 钴湿法冶炼萃取过程中的组分含量软测量[J]. 控制与决策, 2009, 24(4): 632-646. (JIA R D, MAO Z Z, CHANG Y Q, et al. Soft sensing of component content in cobalt hydrometallurgy extraction process[J]. Control and Decision, 2009, 24(4): 632-646.) [4]王雅琳, 桂卫华, 阳春华, 等. 基于有限信息的铜吹炼动态过程智能集成建模[J]. 控制理论与应用, 2009, 26(8): 860-866. (WANG Y L, GUI W H, YANG C H, et al. Intelligent integrated modeling for the dynamic copper-converting process based on limited data information[J]. Control Theory & Applications, 2009, 26(8): 860-866.) [5]晏密英, 桂卫华, 王凌云. 基于神经网络补偿灰色预测误差的钴离子浓度预测研究[J]. 计算机与应用化学, 2008, 25(7): 805-808. (YAN M Y, GUI W H, WANG L Y. Research of prediction in cobalt ions concentration based on a neural network compensating the error of grey forecast[J]. Computers and Applied Chemistry, 2008, 25(7), 805-808.) [6]Vapnik V. The nature of statistical learning[M]. New York: Springer, 1995. [7]张英, 苏宏业, 褚健. 基于模糊最小二乘支持向量机的软测量建模[J]. 控制与决策, 2005, 20(6): 621-624. (ZHANG Y, SU H Y, CHU J. Soft sensor modeling based on fuzzy least squares support vector machines[J]. Control and Decision, 2005, 20(6): 621-624.) [8]梁绍华, 郑立刚, 周昊, 岑可法. 基于支持向量机与高斯分布估计的低NOX排放[J]. 化工学报, 2009, 60(1): 223-.229. (LIANG S H, ZHENG L G, ZHOU H, CEN K F. Low NOX emissions based on support vector machine and Gaussian estimation of distribution[J]. CIESC Journal, 2009, 60(1): 223-.229.) [9]王凌云, 桂卫华, 刘梅花, 等. 基于改进在线支持向量机回归的离子浓度预测模型[J]. 控制与决策, 2009, 24(4): 537-541. (WANG L Y, GUI W H, LIU M H, et al. Prediction model of ion concentration based on improved online support vector regression[J]. Control and Decision, 2009, 24(4): 537-541.) [10]CHEN X, LI Y M. A modified PSO structure resulting in high exploration ability with convergence guaranteed[J]. IEEE TRANSACTION ON SYSTEMS, MAN, AND CYBERNETICS, 2007, 37(5): 1271-1289. [11]Cherkassky.V, Ma.Y. Practical Selection of SVM Parameters and Noise Estimation for SVM Regression[J]. Neural Network, 2004, 17(1): 113-126. [11]王雅琳. 智能集成建模理论及其在有色冶炼过程优化控制中的应用研究[D]. 长沙: 中南大学, 2001. (WANG Y L. Research on theory of intelligent integrated modeling and its applications to optimization and control of nonferrous metallurgical process[D]. Changsha, Central South University, 2001.) [12]FU X P, ZOU M. Application of combination weighting method in contract risk’s evaluation of third party logistics[J]. Journal of Southeast University, 2007, 23(S1): 128-132