[1] Su C H, Cheng C H. A hybrid fuzzy time series model based on ANFIS and integrated nonlinear feature selection method for forecasting stock. Neurocomputing, 2016, 205: 264−273 doi: 10.1016/j.neucom.2016.03.068
[2] Haidar A, Verma B. A novel approach for optimizing climate features and network parameters in rainfall forecasting. Soft Computing, 2018, 22(24): 8119−8130 doi: 10.1007/s00500-017-2756-7
[3] Arthun M, Eldevik T, Viste E, Drange H, Furevik T, Johnson H L, et al. Skillful prediction of northern climate provided by the ocean. Nature Communications, 2017, 8: 15875 doi: 10.1038/ncomms15875
[4] Wang X, Jiang R, Li L, Lin Y, Zheng X, Wang F. Capturing car-following behaviors by deep learning. IEEE Transactions on Intelligent Transportation Systems, 2018, 19(3): 910−920 doi: 10.1109/TITS.2017.2706963
[5] 周平, 刘记平. 基于数据驱动多输出ARMAX建模的高炉十字测温中心温度在线估计. 自动化学报, 2018, 44(3): 552−561

Zhou Ping, Liu Ji-Ping. Data-driven multi-output ARMAX model for online estimation of central temperatures for cross temperature measuring in blast furnace ironmaking. Acta Automatica Sinica, 2018, 44(3): 552−561
[6] 张颜颜, 唐立新. 改进的数据驱动子空间算法求解钢铁企业能源预测问题. 控制理论与应用, 2012, 29(12): 1616−1622

Zhang Yan-Yan, Tang Li-Xin. Improved data-driven subspace algorithm for energy prediction in iron and steel industry. Control Theory and Application, 2012, 29(12): 1616−1622
[7] 郑念祖, 丁进良. 基于Regression GAN的原油总氢物性预测方法. 自动化学报, 2018, 44(5): 915−921

Zheng Nian-Zu, Ding Jin-Liang. Regression GAN based prediction for physical of total hydrogen in crude oil. Acta Automatica Sinica, 2018, 44(5): 915−921
[8] Hearst M A, Dumais S T, Osuna E, Platt J, Scholkopf B. Support vector machines. IEEE Intelligent Systems and Their Applications, 1998, 13(4): 18−28 doi: 10.1109/5254.708428
[9] Trappey A J, Hsu F C, Trappey C V, Lin C I. Development of a patent document classification and search platform using a back-propagation network. Expert Systems with Applications, 2006, 31(4): 755−765 doi: 10.1016/j.eswa.2006.01.013
[10] Huang G B, Zhu Q Y, Siew C K. Extreme learning machine: Theory and application. Neurocomputing, 2006, 70: 489−502 doi: 10.1016/j.neucom.2005.12.126
[11] Jaeger H, Haas H. Harnessing nonlinearity: Predicting chaotic systems and saving energy in wireless communication. Science, 2004, 304(5667): 78−80 doi: 10.1126/science.1091277
[12] 韩敏, 任伟杰, 许美玲. 一种基于L1范数正则化的回声状态网络. 自动化学报, 2014, 40(11): 2428−2435

Han Min, Ren Wei-Jie, Xu Mei-Ling. An improved echo state network via L1-norm regularization. Acta Automatica Sinica, 2014, 40(11): 2428−2435
[13] 伦淑娴, 林健, 姚显双. 基于小世界回声状态网的时间序列预测. 自动化学报, 2015, 41(9): 1669−1679

Lun Shu-Xian, Lin Jian, Yao Xian-Shuang. Time series prediction with an improved echo state network using small world network. Acta Automatica Sinica, 2015, 41(9): 1669−1679
[14] Han M, Xu M. Laplacian echo state network for multivariate time series prediction. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(1): 238−244 doi: 10.1109/TNNLS.2016.2574963
[15] 李德才, 韩敏. 基于鲁棒回声状态网络的混沌时间序列预测研究. 物理学报, 2011, 60(10): 108903 doi: 10.7498/aps.60.108903

Li De-Cai, Han Min. Chaotic time series prediction based on robust echo state network. Acta Physica Sinica, 2011, 60(10): 108903 doi: 10.7498/aps.60.108903
[16] 韩敏, 王亚楠. 基于Kalman滤波的储备池多元时间序列在线预报器. 自动化学报, 2010, 36(1): 169−173 doi: 10.3724/SP.J.1004.2010.00169

Han Min, Wang Ya-Nan. Multivariate time series online predictor with Kalman filter trained reservoir. Acta Automatica Sinica, 2010, 36(1): 169−173 doi: 10.3724/SP.J.1004.2010.00169
[17] Xu D, Lan J, Principe J C. Direct adaptive control: an echo state network and genetic algorithm approach[C]. In: Proceedings of the 2005 IEEE International Joint Conference on Neural Networks. Montreal, Canada: IEEE, 2005. 3: 1483−1486
[18] Wang J S, Han S, Guo Q P. Echo state networks based predictive model of vinyl chloride monomer convention velocity optimized by artificial fish swarm algorithm. Soft Computing, 2014, 18(3): 457−468 doi: 10.1007/s00500-013-1068-9
[19] Chouikhi N, Ammar B, Rokbani N, Alimi A M. PSO-based analysis of echo state network parameters for time series forecasting. Applied Soft Computing, 2017, 55: 211−225 doi: 10.1016/j.asoc.2017.01.049
[20] 孙晓燕, 陈姗姗, 巩敦卫, 张勇. 基于区间适应值交互式遗传算法的加权多输出高斯过程代理模型. 自动化学报, 2014, 40(2): 172−184

Sun Xiao-Yan, Chen Shan-Shan, Gong Dun-Wei, Zhang Yong. Weighted multi-output Gaussian process-based surrogate of interactive genetic algorithm with individual′s interval fitness. Acta Automatica Sinica, 2014, 40(2): 172−184
[21] Du W, Ying W, Yan G, Zhu Y, Cao X. Heterogeneous strategy particle swarm optimization. IEEE Transactions on Circuits and Systems II: Express Briefs, 2017, 64(4): 467−471 doi: 10.1109/TCSII.2016.2595597
[22] Mavrovouniotis M, Muller F M, Yang S. Ant colony optimization with local search for dynamic traveling salesman problems. IEEE Transactions on Cybernetics, 2017, 47(7): 1743−1756 doi: 10.1109/TCYB.2016.2556742
[23] Zhang Z, Wang K, Zhu L, Wang Y. A Pareto improved artificial fish swarm algorithm for solving a multi-objective fuzzy disassembly line balancing problem. Expert Systems with Applications, 2017, 86: 165−176 doi: 10.1016/j.eswa.2017.05.053
[24] 胡蓉, 钱斌. 一种求解随机有限缓冲区流水线调度的混合差分进化算法. 自动化学报, 2009, 35(12): 1580−1586

Hu Rong, Qian Bin. A hybrid differential evolution algorithm for stochastic flow shop scheduling with limited buffers. Acta Automatica Sinica, 2009, 35(12): 1580−1586
[25] Rao R V, Savsani V J, Vakharia D P. Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems. Computer-Aided Design, 2011, 43: 303−315 doi: 10.1016/j.cad.2010.12.015
[26] Xu M L, Han M, Lin H F. Wavelet-denoising multiple echo state networks for multivariate time series prediction. Information Sciences, 2018, 465: 439−458 doi: 10.1016/j.ins.2018.07.015
[27] 周晓根, 张贵军, 郝小虎. 局部抽象凸区域剖分差分进化算法. 自动化学报, 2015, 41(7): 1315−1327

Zhou Xiao-Gen, Zhang Gui-Jun, Hao Xiao-Hu. Differential evolution algorithm with local abstract convex region partition. Acta Automatica Sinica, 2015, 41(7): 1315−1327
[28] 丁进良, 杨翠娥, 陈立鹏, 柴天佑. 基于参考点预测的动态多目标优化算法. 自动化学报, 2017, 43(2): 313−320

Ding Jin-Liang, Yang Cui-E, Chen Li-Peng, Chai Tian-You. Dynamic multi-objective optimization algorithm based on reference point prediction. Acta Automatica Sinica, 2017, 43(2): 313−320
[29] Zhang J Q, Sanderson A C. JADE: Adaptive differential evolution with optional external archive. IEEE Transactions on Evolutionary Computation, 2009, 13(5): 945−958 doi: 10.1109/TEVC.2009.2014613
[30] Zhao S Z, Suganthan P N, Das S. Self-adaptive differential evolution with multi-trajectory search for large-scale optimization. Soft Computing, 2011, 15: 2175−2185 doi: 10.1007/s00500-010-0645-4