·1060· 工程科学学报，第41卷，第8期 ings of ICNN95-International Conference on Neural Netcorks. Chin J Eng,2018,40(7):815 Perth,1995:1942 (陈恒志，杨建平，卢新春，等.基于极限学习机(ELM)的连 [15]Li Q,Xu Y M,Zhang D Z,et al.Global path planning method 铸坯质量预测.工程科学学报，2018,40(7)：815) for mobile robots based on the particle swarm algorithm.IUni [19]Na W B.Su Z W,Ji Y F.Research ofsingle well production pre- Sci Technol Beijing,2010,32(3):397 diction based on improved extreme learning machine.Appl Mech (李擎，徐银梅，张德政，等.基于粒子群算法的移动机器人 Mater,2013,333-335:1296 全局路径规划策路.北京科技大学学报，2010,32(3)：397) [20]Martinez-Martinez J M,Escandell-Montero P,Soria-Olivas E,et [16]Tao HL.Study on Forecast of Raiheay Traffic Volume Based on al.Regularized extreme learning machine for regression prob- Hybrid Intelligent Algorithm Dissertation ]Lanzhou:Lanzhou lems.Neurocomputing,2011.74(17):3716 Jiaotong University,2012 [21]Hornik K,Stinchcombe M,White H.Multilayer feedforward net- (陶海龙.基于混合智能算法的铁路运量预测研究[学位论 works are universal approximators.Neural Netcorks,1989,2 文].兰州：兰州交通大学，2012) (5):359 [17]Bueno-Crespo A,Garcia-Laencina P J,Sancho-G6mez J L.Neu- [22]Du B,Lin Y.Development andapplication of an universal auto- ral architecture design based on extreme learning machine.Neu- matic modeling tools.Comput Technol Autom,2003,22(2): ral Nettorks,2013,48:19 103 [18]Chen HZ.Yang J P.Lu X C.et al.Quality prediction of the (杜斌，林云.通用智能自动建模软件开发与应用.计算技 continuous casting bloom based on the extreme learning machine. 术与自动化，2003.22(2)：103)工程科学学报,第 41 卷,第 8 期 ings of ICNN蒺 95—International Conference on Neural Networks. Perth, 1995: 1942 [15] Li Q, Xu Y M, Zhang D Z, et al. Global path planning method for mobile robots based on the particle swarm algorithm. J Univ Sci Technol Beijing, 2010, 32(3): 397 (李擎, 徐银梅, 张德政, 等. 基于粒子群算法的移动机器人 全局路径规划策略. 北京科技大学学报, 2010, 32(3): 397) [16] Tao H L. Study on Forecast of Railway Traffic Volume Based on Hybrid Intelligent Algorithm [ Dissertation]. Lanzhou: Lanzhou Jiaotong University, 2012 (陶海龙. 基于混合智能算法的铁路运量预测研究[学位论 文]. 兰州: 兰州交通大学, 2012) [17] Bueno鄄Crespo A, Garc侏a鄄Laencina P J, Sancho鄄G佼mez J L. Neu鄄 ral architecture design based on extreme learning machine. Neu鄄 ral Networks, 2013, 48: 19 [18] Chen H Z, Yang J P, Lu X C, et al. Quality prediction of the continuous casting bloom based on the extreme learning machine. Chin J Eng, 2018, 40(7): 815 (陈恒志, 杨建平, 卢新春, 等. 基于极限学习机(ELM)的连 铸坯质量预测. 工程科学学报, 2018, 40(7): 815) [19] Na W B, Su Z W, Ji Y F. Research ofsingle well production pre鄄 diction based on improved extreme learning machine. Appl Mech Mater, 2013, 333鄄335: 1296 [20] Mart侏nez鄄Mart侏nez J M, Escandell鄄Montero P, Soria鄄Olivas E, et al. Regularized extreme learning machine for regression prob鄄 lems. Neurocomputing, 2011, 74(17): 3716 [21] Hornik K, Stinchcombe M, White H. Multilayer feedforward net鄄 works are universal approximators. Neural Networks, 1989, 2 (5): 359 [22] Du B, Lin Y. Development andapplication of an universal auto鄄 matic modeling tools. Comput Technol Autom, 2003, 22 ( 2 ): 103 (杜斌, 林云. 通用智能自动建模软件开发与应用. 计算技 术与自动化, 2003, 22(2): 103) ·1060·