[1] 于同洋, 肖人彬, 侯俊东. 网络舆情结构逆转建模与仿真:基于改进Deffuant模型[J]. 复杂系统与复杂性科学, 2019, 16(3): 30-39.
YU T Y, XIAO R B, HOU J D. Modeling and simulation of network public opinion structure reversal: based on an improved Deffuant model[J]. Complex Systems and Complexity Science, 2019, 16 (3): 30-39.
[2] 张轩宇, 陈曦, 肖人彬. 后真相时代基于敌意媒体效应的观点演化建模与仿真[J]. 复杂系统与复杂性科学, 2023, 20(3): 44-51,59.
ZHANG X Y, CHEN X, XIAO R B. Modeling and simulation of viewpoint evolution based on hostile media effect in the post truth era [J]. Complex Systems and Complexity Science, 2023, 20(3): 44-51,59.
[3] 刘举胜, 何建佳, 韩景倜, 等. 观点动力学研究现状及进展述评[J]. 复杂系统与复杂性科学, 2021, 18(2): 9-20.
LIU J S, HE J J, HAM J T, et al. Review on the research status and progress of viewpoint dynamics[J]. Complex Systems and Complexity Science, 2021, 18 (2): 9-20
[4] 张和平, 陈齐海. 基于灰色马尔可夫模型的网络舆情预测研究[J]. 情报科学, 2018, 36(1): 75-79.
ZHANG H P, CHEN Q H. Research on internet public opinion prediction based on grey Markov model [J]. Information Science, 2018, 36(1): 75-79.
[5] Anjaria M, Guddeti R. Influence factor-based opinion mining of twitter data using supervised learning[C]//2014 Sixth International Conference on Communication Systems and Networks (COMSNETS). Bangalore, India: IEEE, 2014.
[6] 林玲, 陈福集, 谢加良, 等. 基于改进灰狼优化支持向量回归的网络舆情预测[J]. 系统工程理论与实践, 2022,42(2): 487-498.
LIN L, CHEN F J, XIE J L,et al. Network public opinion prediction based on improved grey wolf optimization support vector regression[J]. Systems Engineering Theory and Practice, 2022, 42(2): 487-498.
[7] 野雪莲, 杨孔雨. 舆情趋势预测中神经网络的优化算法[J]. 网络新媒体技术, 2016, 5(1): 33-37,51.
YE X L,YANG K Y. Neural network optimization algorithm in public opinion trends prediction[J]. Journal of Network New Media, 2016, 5(1): 33-37,51.
[8] 孙靖超, 周睿, 李培岳, 等. 基于循环神经网络的网络舆情趋势预测研究[J]. 情报科学, 2018, 36(8):118-122,127.
SUN J C, ZHOU R, LI P Y,et al. Research on network public opinion trend prediction based on recurrent neural network[J]. Information Science, 2018, 36(8): 118-122,127.
[9] 傅丽芳, 赵菲菲. 基于注意力机制LSTM模型的农业舆情预测与分析[J]. 数学的实践与认识, 2021, 51(17): 64-76.
FU L F, ZHAO F F. A forecasting method of agricultural public opinion based on Lda-Lstm-Attn model[J]. Journal of Mathematics in Practice and Theory, 2021, 51(17): 64-76.
[10] 魏腾飞, 潘庭龙. 基于改进PSO优化LSTM网络的短期电力负荷预测[J]. 系统仿真学报, 2021, 33(8): 1866-1874.
WEI T F, PAN T L.Short-term power load forecasting based on LSTM neural network optimized by improved PSO[J]. Journal of System Simulation, 2021, 33(8): 1866-1874.
[11] 于永进, 姜雅男, 李长云. 基于WOA-LSTM模型的机-热老化绝缘纸剩余寿命预测方法[J]. 电工技术学报, 2022, 37(12): 3162-3171.
YU Y J, JIANG Y N, LI C Y.Prediction method of insulation paper remaining life with mechanical-thermal synergy based on whale optimization algorithm-Long-Short Term Memory model[J]. Transactions of China Electrotechnical Society, 2022, 37(12):3162-3171.
[12] 吴虎胜, 张凤鸣, 吴庐山. 一种新的群体智能算法——狼群算法[J]. 系统工程与电子技术, 2013, 35(11): 2430-2438.
WU H S, ZHANG F M, WU L S. A new swarm intelligence algorithm—wolf packalgorithm[J]. Systems Engineering and Electronics, 2013, 35(11): 2430-2438.
[13] 薛俊杰, 王瑛, 李浩, 等. 一种狼群智能算法及收敛性分析[J]. 控制与决策, 2016, 31(12):2131-2139.
XUE JJ, WANG Y, LI H, et al. A smart wolf pack algorithm and its convergence analysis[J]. Control and Decision, 2016, 31 (12): 2131-2139.
[14] 叶勇, 张惠珍. 多配送中心车辆路径问题的狼群算法[J]. 计算机应用研究, 2017,34(9):36-39.
YE Y, ZHANG H Z.Wolf pack algorithm for multi-depot vehicle routing problem[J]. Application Research of Computers, 2017,34(9): 36-39.
[15] 尹怀仙, 王凯, 张铁柱, 等. 基于PSO-BP神经网络的城轨列车转向架轮对轴箱故障预测[J]. 复杂系统与复杂性科学, 2015, 12(4): 97-103.
YIN H X, WANG K, ZHANG T Z,et al. Fault prediction based on PSO-BP neural network about wheel and axle box of bogie in urban rail train[J]. Complex Systems and Complexity Science, 2015, 12(4): 97-103
[16] 胡亮, 肖人彬, 王英聪. 蜂群激发抑制算法及其在交通信号配时中的应用[J]. 复杂系统与复杂性科学, 2019, 16(2): 9-18,40.
HU L, XIAO R B, WANG Y C.Bee colony activating-inhibition algorithm and its application in traffic signal timing[J]. Complex Systems and Complexity Science, 2019, 16(2): 9-18,40.
[17] 韩忠华, 刘约翰, 李曼, 等. 改进狼群算法求解模具在模台上组合分配问题[J]. 系统仿真学报, 2021, 33(1): 127-140.
HAN Z H, LIU Y H, LI M, SUN LL, et al. Improved wolf pack algorithm for distribution of molds on molds table[J]. Journal of System Simulation, 2021, 33(1): 127-140.
[18] WU H S, ZHANG F M. A uncultivated wolf pack algorithm for high-dimensional functions and its application in parameters optimization of PID controller[C]//2014 IEEE Congress on Evolutionary Computation (CEC). Beijing, China: IEEE, 2014.
[19] 刘东阳, 查文文, 陶亮, 等. 基于LSTM和SMC的农用履带机器人轨迹跟踪控制[J]. 系统仿真学报, 2023, 35(4): 747-759.
LIU D Y, ZHA WW, TAO L, et al. Trajectory control of crawler robot based on LSTM and SMC [J]. Journal of System Simulation, 2023, 35(4): 747-759.
[20] EOM G, BYEON H. Development of keyword trend prediction models for obesity before and after the Covid-19 pandemic using RNN and LSTM: analyzing the news big data of South Korea[J]. Frontiers in Public Health, 2022, 10: 894266.
[21] DU B G, HUANG S, GUO J, et al. Interval forecasting for urban water demand usingPSO optimized KDE distribution and LSTM neural networks[J]. Applied Soft Computing, 2022, 122: 108875.
[22] 陈暄, 孟凡光, 吴吉义. 求解大规模优化问题的改进狼群算法[J]. 系统工程理论与实践, 2021, 41(3): 790-808.
CHEN X, MENG F G, WU J Y.Improved wolf pack algorithm for large-scale optimization problems[J]. Systems Engineering Theory and Practice, 2021, 41(3): 790-808.
[23] GAO Y J, ZHANG F M, ZHAO Y, et al. Quantum-inspired wolf pack algorithm to solve the 0-1 knapsack problem[J]. Mathematical Problems in Engineering, 2018(4): 5327056.
[24] 刘成汉, 何庆. 融合多策略的黄金正弦黑猩猩优化算法[J]. 自动化学报, 2023, 49(11): 2360-2373.
LIU C H, HE Q.Golden sine chimp optimization algorithm integrating multiple strategies[J]. Acta Automatica Sinica, 2023, 49(11): 2360-2373.
[25] MIRJALILI S, LEWIS A. The whale optimization algorithm[J]. Advances in Engineering Software, 2016, 95: 51-67.
[26] 罗辞勇, 陈民铀, 张聪誉. 采用循环拥挤排序策略的改进NSGA-Ⅱ算法[J]. 控制与决策, 2010, 25(2): 227-231.
LUO C Y, CHEN M Y, ZHANG C Y.Improved NSGA-Ⅱ algorithm with circular crowded sorting[J]. Control and Decision, 2010, 25(2): 227-231.
[27] 王艳娇, 马春蕾. 基于记忆策略的动态离子运动优化算法[J]. 吉林大学学报(工学版), 2020, 50(3): 1047-1060.
WANG Y J, MA C L.Dynamic ion motion optimization algorithm based on memory strategy[J]. Journal of Jilin University (Engineering and Technology Edition), 2020, 50(3): 1047-1060.
[28] 汪慎文, 丁立新, 谢承旺, 等. 应用精英反向学习策略的混合差分演化算法[J]. 武汉大学学报(理学版), 2013, 59(2): 111-116.
WANG S W, DING L X, XIE C W, et al. A hybrid differential evolution with elite opposition-based learning[J]. Journal of Wuhan University (Natural Science Edition), 2013, 59(2): 111-116.
[29] HUANG Z H, WEI X, KAI Y. Bidirectional LSTM-CRF models for sequencetagging[DB/OL]. [2022-01-30]. https://doi.org/10.48550/arXiv.1508.01991
[30] 王鑫, 吴际, 刘超, 等. 基于LSTM循环神经网络的故障时间序列预测[J]. 北京航空航天大学学报, 2018, 44(4): 772-784.
WANG X, WU J, LIU C, et al.Exploring LSTM-based recurrent neural network for failure time series prediction[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(4): 772-784.
[31] XIAO R B, YU T Y, HOU J D. Modeling and simulation of opinion natural reversal dynamics with opinion leader based on HK bounded confidence model[J]. Complexity, 2020(1): 7360302.
[32] 刘琪, 肖人彬. 观点动力学视角下基于意见领袖的网络舆情反转研究[J]. 复杂系统与复杂性科学, 2019, 16(1): 1-13.
LIU Q, XIAO R B. An opinion dynamics approach to public opinion reversion with the guidance of opinion leaders[J]. Complex Systems and Complexity Science, 2019, 16(1): 1-13.
[33] 刘巧玲, 李劲, 肖人彬. 基于参数反演的网络舆情传播趋势预测——以新浪微博为例[J]. 计算机应用, 2017, 37(5): 1419-1423.
Liu Q L, Li J, Xiao R B.Trend prediction of public opinion propagation based on parameter inversion—an empirical study on Sina micro-blog [J]. Journal of Computer Applications, 2017, 37(5): 1419-1423.
[34] WU H S, XIAO R B.Flexible wolf pack algorithm for dynamic multidimensional knapsack problems[J]. Research, 2020(1): 174-186.
[35] LI J H, ZHU D S, LI C X. Comparative analysis of BPNN, SVR, LSTM, Random Forest, and LSTM-SVR for conditional simulation of non-Gaussian measured fluctuating wind pressures[J]. Mechanical Systems & Signal Processing, 2022, 178: 109285.
