[1] 刘业翔,李劼.现代铝电解[M].北京:冶金工业出版社,2008:192-214.(LIU Y X, LI J. Modern Aluminum Electrolysis[M]. Beijing:Metallurgical Industry Press, 2008:192-214.) [2] SLISKOVIC D, GRBIC R, HOCENSKI Z. Methods for plant data-based process modeling in soft-sensor development[J]. Automatika, 2012, 52(4):306-318. [3] ZHANG H, KUANG Y, WANG G, et al. Soft sensor model for coal slurry ash content based on image gray characteristics[J]. International Journal of Coal Preparation and Utilization, 2014, 34(1):24-37. [4] YAN X, YANG W, MA H, et al. Soft sensor for ammonia concentration at the ammonia converter outlet based on an improved group search optimization and BP neural network[J]. Chinese Journal of Chemical Engineering, 2012, 20(6):1184-1190. [5] KANEKO H, FUNATSU K. Application of online support vector regression for soft sensors[J]. AICHE Journal, 2014, 60(2):600-612. [6] YU J. A Bayesian inference based two-stage support vector regression framework for soft sensor development in batch bioprocesses[J]. Computers and Chemical Engineering, 2012, 41(12):134-144. [7] HUANG G B, ZHU Q Y, SIEW C K. Extreme learning machine:theory and applications[J]. Neurocomputing, 2006, 70(1/2/3):489-501. [8] DING S, ZHAO H, ZHANG Y, et al. Extreme learning machine:algorithm, theory and applications[J]. Artificial Intelligence Review, 2015, 44(1):103-115. [9] HAN F, HUANG D S. Improved extreme learning machine for function approximation by encoding apriori information[J]. Neurocomputing, 2006, 69(16/17/18):2369-2373. [10] ZHANG H, YIN Y, ZHANG S. An improved ELM algorithm for the measurement of hot metal temperature in blast furnace[J]. Neurocomputing, 2016, 174:232-237. [11] YANG Y, ZHANG S, YIN Y. A modified ELM algorithm for the prediction of silicon content in hot metal[J]. Neural Computing and Applications, 2016, 27(1):241-247. [12] 常玉清,李玉朝,王福利,等.基于极限学习机的生化过程软测量建模[J].系统仿真学报,2007,19(23):5587-5590.(CHANG Y Q, LI Y C, WANG F L, et al. Soft sensing modeling based on extreme learning machine for biochemical processes[J]. Journal of System Simulation, 2007, 19(23):5587-5590.) [13] 张旖芮,阳春华,朱红求.基于数据的铝电解槽况分类[J].计算机工程与应用,2015,51(11):233-237.(ZHANG Y R, YANG C H, ZHU H Q. Classification of cell states for aluminum electrolysis based on data[J]. Computer Engineering and Applications, 2015, 51(11):233-237.) [14] SERRE D. Matrices[M]. New York:Springer, 2002:35. [15] 贺彦林,王晓,朱群雄.基于主成分分析-改进的极限学习机方法的精对苯二甲酸醋酸含量软测量[J].控制理论与应用,2015,32(1):80-85.(HE Y L, WANG X, ZHU Q X. Modeling of acetic acid content in purified terephthalic acid solvent column using principal component analysis based improved extreme learning machine[J]. Control Theory and Applications, 2015, 32(1):80-85.) [16] 王海燕,杨方廷,刘鲁.标准化系数与偏相关系数的比较与应用[J].数量经济技术经济研究,2006,23(9):150-155.(WANG H Y, YANG F T, LIU L. Comparison and application of standardized regressive coefficient and partial correlation coefficient[J]. The Journal of Quantitative and Technical Economics, 2006, 23(9):50-155.) [17] 郝黎仁,樊元,郝哲欧.SPSS实用统计分析[M].北京:中国水利水电出版社,2007:184.(HAO N R, FAN Y, HAO Z O. Practical Statistical Analysis of SPSS[M]. Beijing:China Water and Power Press, 2007:184.) [18] PAWLAK Z, SKOWRON A. Rudiments of rough sets[J]. Information Sciences, 2007, 177(1):3-27. [19] 王国胤,姚一豫,于洪.粗糙集理论与应用研究综述[J].计算机学报,2009,32(7):1229-1246.(WANG G Y, YAO Y Y, YU H. A survey on rough set theory and applications[J]. Chinese Journal of Computers, 2009, 32(7):1229-1246.) [20] 王国胤.Rough集理论与知识获取[M].西安:西安交通大学出版社,2001:133-134.(WANG G Y. Rough Set Theory and Knowledge Acquisition[M]. Xi'an:Xi'an Jiaotong University Press, 2001:133-134.) [21] 刘咏,任凤莲.BP神经网络在铝电解质分子比预报中的应用[J].冶金分析,2006,26(4):28-31.(LIU Y, REN F L. Application of BP neural network in prediction of molecular ratio of aluminum electrolyte[J]. Metallurgical Analysis, 2006, 26(4):28-31.) |