全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

多元混沌时间序列的因子回声状态网络预测模型

DOI: 10.16383/j.aas.2015.c140604, PP. 1042-1046

Keywords: 多元混沌时间序列,预测,回声状态网络,因子分析

Full-Text   Cite this paper   Add to My Lib

Abstract:

?针对采用回声状态网络预测多元混沌时间序列时存在的病态解问题,本文建立了因子回声状态网络模型,通过因子分析(Factoranalysis,FA)方法提取高维储备池状态矩阵的公因子,去除冗余和噪声成分.利用降维后的因子变量与期望输出之间的线性回归关系,求解网络未知参数.基于Lorenz序列和大连月平均气温--降雨量的仿真实验验证了本文所提模型的有效性.

References

[1]  Varadhan R, Roland C. Simple and globally convergent methods for accelerating the convergence of any EM algorithm. Scandinavian Journal of Statistics, 2008, 35(2): 335-353
[2]  Mirmomeni M, Lucas C, Araabi B N, Moshiri B, Bidar M R. Recursive spectral analysis of natural time series based on eigenvector matrix perturbation for online applications. IET Signal Processing, 2011, 5(6): 515-526
[3]  van der Maaten L J P, Postm E O, van den Herik H J. Dimensionality Reduction: a Comparative Review. Technical Report TiCC-TR 2009-005, Tilburg University, Tilburg, The Netherlands, 2009.
[4]  Rong T Z, Xiao Z. Nonparametric interval prediction of chaotic time series and its application to climatic system. International Journal of Systems Science, 2013, 44(9): 1726-1732
[5]  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)
[6]  Inoussa G, Peng H, Wu J. Nonlinear time series modeling and prediction using functional weights wavelet neural network-based state-dependent AR model. Neurocomputing, 2012, 86(1): 59-74
[7]  Li P H, Li Y G, Xiong Q Y, Chai Y, Zhang Y. Application of a hybrid quantized Elman neural network in short-term load forecasting. International Journal of Electrical Power & Energy Systems, 2014, 55: 749-759
[8]  Yeh W C. New parameter-free simplified swarm optimization for artificial neural network training and its application in the prediction of time series. IEEE Transactions on Neural Networks and Learning Systems, 2013, 24(4): 661-665
[9]  Xuan Zhao-Yan, Yang Gong-Xun. Application of EMD in the atmoshere time series prediction. Acta Automatica Sinica, 2008, 34(1): 97-101(玄兆燕, 杨公训. 经验模态分解法在大气时间序列预测中的应用. 自动化学报, 2008, 34(1): 97-101)
[10]  Zeng Z G, Wang J. Improved conditions for global exponential stability of recurrent neural networks with time-varying delays. IEEE Transactions on Neural Networks, 2006, 17(3): 623-635
[11]  Zhang H G, Liu J H, Ma D Z, Wang Z S. Data-core-based fuzzy min-max neural network for pattern classification. IEEE Transactions on Neural Networks, 2011, 22(12): 2339-2352
[12]  Zhang H G, Liu D R, Luo Y H, Wang D. Adaptive Dynamic Programming for Control: Algorithms and Stability. London: Springer, 2013. 1-19
[13]  Jaeger H. The "Echo State" Approach to Analysing and Training Recurrent Neural Networks —— with An Erratum Note, GMD Report 148, German National Research Center for Information Technology, Germany, 2001.
[14]  Jaeger H, Haas H. Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication. Science, 2004, 304(5667): 78-80
[15]  Massar M, Massar S. Mean-field theory of echo state networks. Physical Review E, 2013, 87(4): 042809
[16]  Luko?sevi?ius M, Jaeger H, Schrauwen B. Reservoir computing trends. Kl-Künstliche Intelligenz, 2012, 26(4): 365-371
[17]  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)
[18]  Shi Z W, Han M. Support vector echo-state machine for chaotic time-series prediction. IEEE Transactions on Neural Networks, 2007, 18(2): 359-372
[19]  Chatzis S P, Demiris Y. Echo state Gaussian process. IEEE Transactions on Neural Networks, 2011, 22(9): 1435-1445
[20]  Han Min, Wang Ya-Nan. Prediction of multivariate time series based on reservoir principal component analysis. Control and Decision, 2009, 24(10): 1526-1530(韩敏, 王亚楠. 基于储备池主成分分析的多元时间序列预测研究. 控制与决策, 2009, 24(10): 1526-1530)
[21]  Ghahramani Z, Hinton G E. The EM Algorithm for Mixtures of Factor Analyzers, Technical Report CRG-TR-96-1, University of Toronto, Canada, 1996.
[22]  He Liang, Shi Yong-Zhe, Liu Jia. Eigenchannel space combination method of joint factor analysis. Acta Automatica Sinica, 2011, 37(7): 849-856(何亮, 史永哲, 刘加. 联合因子分析中的本征信道空间拼接方法. 自动化学报, 2011, 37(7): 849-856)
[23]  Meng Qing-Fang, Peng Yu-Hua, Qu Huai-Jing, Han Min. The neighbor point selection method for local prediction based on information criterion. Acta Physica Sinica, 2008, 57(3): 1423-1430(孟庆芳, 彭玉华, 曲怀敬, 韩民. 基于信息准则的局域预测法邻近点的选取方法. 物理学报, 2008, 57(3): 1423-1430)
[24]  Yang Xu-Kui, Qu Dan, Zhang Wen-Lin. An orthogonal Laplacian language recognition approach. Acta Automatica Sinica, 2014, 40(8): 1812-1818(杨绪魁, 屈丹, 张文林. 正交拉普拉斯语种识别方法. 自动化学报, 2014, 40(8): 1812-1818)

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133