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基于小世界回声状态网的时间序列预测

DOI: 10.16383/j.aas.2015.c150049, PP. 1669-1679

Keywords: 回声状态网,小世界网络,时间序列预测,储备池

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Abstract:

?为了提高时间序列的预测精度,提出了利用改进的小世界网络优化泄露积分型回声状态网(Leaky-integratorechostatenetwork,LeakyESN)的时间序列预测方法.首先提出一个改进型小世界网络,其加边概率是节点间距离的负指数函数.然后,利用加边概率直接表示LeakyESN储备池两个神经节点的连接权值,取值范围为[0,1],表征了节点间的连接程度.利用这个新型小世界网络改进LeakyESN的储备池神经节点的连接方式,有目的地实现了稀疏连接,减小了LeakyESN储备池随机稀疏连接的盲目性,提高了储备池的适应性.最后,利用改进的LeakyESN预测典型的非线性时间序列,并利用Matlab仿真软件验证了本文提出方法的有效性.与LeakyESN相比,本文提出的方法具有更高的预测精度和更短的训练时间.

References

[1]  Chen Guan-Rong. Problems and challenges in control theory under complex dynamical network environments. Acta Automatica Sinica, 2013, 39(4): 312-321(陈关荣. 复杂动态网络环境下控制理论遇到的问题与挑战. 自动化学报, 2013, 39(4): 312-321)
[2]  Chen L, Lv J H, Lu J A. Synchronization of the time-varying discrete biological networks. In: Proceedings of the 2007 IEEE International Symposium on Circuit and Systems (ISCAS 07). New Orleans, LA: IEEE, 2007. 2650-2653
[3]  Li Jun, Yue Wen-Qi. Dynamic soft sensor modeling and its application using leaky-integrator ESN. CIESC Journal, 2014, 65(10): 4004-4014(李军, 岳文琦. 基于泄漏积分型回声状态网络的软测量动态建模方法及应用. 化工学报, 2014, 65(10): 4004-4014)
[4]  Jaeger H. The "Echo State" Approach to Analysing and Training Recurrent Neural Networks. GMD Report 148, German National Research Center for Information Technology [Online], available: http://minds.jacobs-university.de/ sites/default/files/uploads/papers/EchoStatesTechRep.pdf. August 19, 2015.
[5]  Lun S X, Wang S, Guo T T, Du C J. An I-V model based on time warp invariant echo state network for photovoltaic array with shaded solar cells. Solar Energy, 2014, 105: 529-541
[6]  Lun S X, Yao X S, Qi H Y, Hu H F. A novel model of leaky integrator echo state network for time-series prediction. Neurocomputing, 2015, 159: 58-66
[7]  Zhou J, Lu J A, Lv J H. Adaptive synchronization of an uncertain complex dynamical network. IEEE Transactions on Automatic Control, 2006, 51(4): 652-656
[8]  Lv J H, Yu X H, Chen G R, Cheng D Z. Characterizing the synchronizability of small-world dynamical networks. IEEE Transactions on Circuits and Systems I: Regular Papers, 2004, 51(4): 787-796
[9]  Chai Yuan, Cui Hong-Yan. A Novel Echo State Networks Architecture [Online], available: http://www.doc88.com/p-8029033313285.html, August 13, 2015.(柴源, 崔鸿雁. 一种新型回声状态网络预测算法, 中国科技论文在线, http://www.doc88.com/p-8029033313285.html, August 13, 2015.)
[10]  Zhou Z J, Hu C H. An effective hybrid approach based on Grey and ARMA for forecasting gyro drift. Chaos, Solitons and Fractals, 2008, 35(3): 525-529
[11]  Rao Yun-Zhang, Xu Shui-Tai, Xiong Ling-Yan. Time series prediction of heavy metal pollution in mining areas based on ARIMA model. Metal Mine, 2010, (6): 142-146(饶运章, 徐水太, 熊灵燕. 基于ARIMA模型的矿区重金属污染时间序列预测. 金属矿山, 2010, (6): 142-146)
[12]  Huo Xiao-Yu, Yang Shi-Jiao, Wu Chang-Zhen. The research of prediction methods and application of chaotic time series based on BPNN. Journal of University of South China (Science and Technology), 2012, 26(2): 26-31(霍晓宇, 杨仕教, 吴长振. 基于BP神经网络的混沌时间序列预测方法及应用研究. 南华大学学报(自然科学版), 2012, 26(2): 26-31)
[13]  Song R Z, Xiao W D, Sun C Y. Optimal tracking control for a class of unknown discrete-time systems with actuator saturation via data-based ADP algorithm. Acta Automatica Sinica, 2013, 39(9): 1413-1420
[14]  Qu Ren-Hui, Song Li-Hua, Di Chao-Sheng. Chaotic time series prediction based on recursive networks. Journal of Jilin University (Information Science Edition), 2008, 26(2): 136-140(曲仁慧, 宋丽华, 邸朝生. 基于递归网络的混沌时间序列预测. 吉林大学学报(信息科学版), 2008, 26(2): 136-140)
[15]  Han Min, Xu Mei-Ling, Ren Wei-Jie. Research on multivariate chaotic time series prediction using mRSM model. Acta Automatica Sinica, 2014, 40(5): 822-829(韩敏, 许美玲, 任伟杰. 多元混沌时间序列的相关状态机预测模型研究. 自动化学报, 2014, 40(5): 822-829)
[16]  Hu Shou-Song, Zhang Zheng-Dao. Fault prediction for nonlinear time series based on neural network. Acta Automatica Sinica, 2007, 33(7): 744-748(胡寿松, 张正道. 基于神经网络的非线性时间序列故障预报. 自动化学报, 2007, 33(7): 744-748)
[17]  Peng Yu, Wang Jian-Min, Peng Xi-Yuan. Researches on time series prediction with echo state networks. Acta Electronica Sinica , 2010, 38(2A): 148-154(彭宇, 王建民, 彭喜元. 基于回声状态网络的时间序列预测方法研究. 电子学报, 2010, 38(2A): 148-154)
[18]  Qiao Jun-Fei, Bo Ying-Chun, Han Guang. Application of ESN-based multi indices dual heuristic dynamic programming on wastewater treatment process. Acta Automatica Sinica, 2013, 39(7): 1146-1151(乔俊飞, 薄迎春, 韩广. 基于ESN的多指标DHP控制策略在污水处理过程中的应用. 自动化学报, 2013, 39(7): 1146-1151)
[19]  Zhao Lu-Sha. Research on Nonlinear Time Series Prediction based on Echo State Networks [Master dissertation], Harbin Institute of Technology, China, 2012.(赵露莎. 基于回声状态网络的非线性时间序列预测方法研究 [硕士学位论文], 哈尔滨工业大学, 中国, 2012.)
[20]  Wang Zhuo-Qun, Sun Zhi-Guo. Method for prediction of multi-scale time series with WDESN. Journal of Electronic Measurement and Instrument, 2010, 24(10): 947-951(王卓群, 孙志国. 一种小波分解回声状态网络时间序列预测方法. 电子测量与仪器学报, 2010, 24(10): 947-951)
[21]  Bohland J W, Minai A A. Efficient associative memory using small-world architecture. Neurocomputing, 2001, 38-40: 489-496
[22]  Jaeger H, Lukovsevivcius M, Popovici D, Siewert U. Optimization and applications of echo state networks with leaky-integrator neurons. Neural Networks, 2007, 20(3): 335-352
[23]  Li Han. Nonlinear Time Series Prediction Based on Echo State Networks [Master dissertation], Dalian University of Technology, China, 2013.(李菡. 基于回声状态网络的非线性时间序列预测研究 [硕士学位论文], 大连理工大学, 中国, 2013.)
[24]  Liebald B. Exploration of Effects of Different Network Topologies on the ESN Signal Crosscorrelation Matrix Spectrum [Master dissertation], International University Bremen, Germany, 2004.
[25]  Wang Juan. Research on Topologies of Echo State Network [Master dissertation], Chongqing University, China, 2013.(王娟. 回声状态网络的拓扑结构研究 [硕士学位论文], 重庆大学, 中国, 2013.)
[26]  Chen Feng-Lan. The Network Security Situation Predicting Technology Based on the Small-World Echo State Network [Master dissertation], Lanzhou University, China, 2014.(陈凤兰. 基于小世界回声状态网络的网络安全态势预测技术研究 [硕士学位论文], 兰州大学, 中国, 2014.)
[27]  Lv J H, Chen G R. A time-varying complex dynamical network model and its controlled synchronization criteria. IEEE Transactions on Automatic Control, 2005, 50(6): 841-846
[28]  Lv Jin-Hu. Mathematical models and synchronization criterions of complex dynamical networks. Systems Engineering Theory and Practice, 2004, 24(4): 17-22, 62(吕金虎. 复杂动力网络的数学模型与同步准则. 系统工程理论与实践, 2004, 24(4): 17-22, 62)

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