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利用神经元拓展正则极端学习机预测时间序列

张弦 王宏力

张弦, 王宏力. 利用神经元拓展正则极端学习机预测时间序列[J]. 北京航空航天大学学报, 2011, 37(12): 1510-1514.
引用本文: 张弦, 王宏力. 利用神经元拓展正则极端学习机预测时间序列[J]. 北京航空航天大学学报, 2011, 37(12): 1510-1514.
Zhang Xian, Wang Hongli. Time series prediction using neuron-expanding regularized extreme learning machine[J]. Journal of Beijing University of Aeronautics and Astronautics, 2011, 37(12): 1510-1514. (in Chinese)
Citation: Zhang Xian, Wang Hongli. Time series prediction using neuron-expanding regularized extreme learning machine[J]. Journal of Beijing University of Aeronautics and Astronautics, 2011, 37(12): 1510-1514. (in Chinese)

利用神经元拓展正则极端学习机预测时间序列

详细信息
  • 中图分类号: TP 277

Time series prediction using neuron-expanding regularized extreme learning machine

  • 摘要: 为实现对于时间序列预测数据的准确预测,提出一种神经元拓展正则极端学习机(NERELM,Neuron-Expanding Regularized Extreme Learning Machine),并研究了其在时间序列预测中的应用.NERELM根据结构风险最小化原理权衡经验风险与结构风险,以逐次拓展隐层神经元的方式自动确定最佳的网络结构,以避免传统神经网络训练过程中需人为确定网络结构的弊端.应用于时间序列的仿真结果表明:NERELM可有效实现对于RELM最佳网络结构的自动确定,具有预测精度高与计算速度快的优点.

     

  • [1] Song Q S,Feng Z R.The hybrid forecasting model based on chaotic mapping,genetic algorithm and support vector machine[J].Expert Systems with Applications,2010,37(2):1776-1783 [2] Jeng J T,Chuang C C,Tao C W.Hybrid SVMR-GPR for modeling of chaotic time series systems with noise and outliers[J].Neurocomputing,2010,73(10-12):1686-1693 [3] Su L Y.Prediction of multivariate chaotic time series with local polynomial fitting[J].Computers & Mathematics with Applications,2010,59(2):737-744 [4] Lau K W,Wu Q H.Local prediction of non-linear time series using support vector regression[J].Pattern Recognition,2008,41(5):1539-1547 [5] Muhammad A F,Zolfaghari S.Chaotic time series prediction with residual analysis method using hybrid Elman NARX neural networks[J].Neurocomputing,2010,73(13-15):2540-2553 [6] Song Q S,Feng Z R.Effects of connectivity structure of complex echo state network on its prediction performance for nonlinear time series[J].Neurocomputing,2010,73(10-12),2177-2185 [7] Fu Y Y,Wu C J,Jeng J T,et al.ARFNNs with SVR for prediction of chaotic time series with outliers[J].Expert Systems with Applications,2010,37(6):4441-4451 [8] Lin C J,Chen C H,Lin C T.A hybrid of cooperative particle swarm optimization and cultural algorithm for neural fuzzy networks and its prediction applications[J].IEEE Transactions on Systems,Man,and Cybernetics,Part C:Applications and Reviews,2008,39(1):55-68 [9] Huang G B,Zhu Q Y,Siew C K.Extreme learning machine:theory and applications[J].Neurocomputing,2006,70(1-3):489-501 [10] Feng G R,Huang G B,Lin Q P,et al.Error minimized extreme learning machine with growth of hidden nodes and incremental learning[J].IEEE Transactions on Neural Networks,2009,20(8):1352-1357 [11] Tang X L,Han M.Partial Lanczos extreme learning machine for single-output regression problems[J].Neurocomputing,2009, 72(13-15):3066-3076 [12] Cao J W,Lin Z P,Huang G B.Composite function wavelet neural networks with extreme learning machine[J].Neurocomputing,2010,73(7-9):1405-1416 [13] Minhas R,Baradarani A,Seifzadeh S,et al.Human action recognition using extreme learning machine based on visual vocabularies[J].Neurocomputing,2010,73(16-18):1906-1917 [14] Huang G B,Ding X J,Zhou H M.Optimization method based extreme learning machine for classification[J].Neurocomputing,2010,74(1-3):155-163 [15] Deng W Y,Zheng Q H,Lian S G,et al.Ordinal extreme learning machine[J].Neurocomputing,2010,74(1-3):447-456 [16] 邓万宇,郑庆华,陈琳,等.神经网络极速学习方法研究[J].计算机学报,2010,33(2):279-287 Deng Wanyu,Zheng Qinghua,Chen Lin,et al.Research on extreme learning of neural networks[J].Chinese Journal of Computers,2010,33(2):279-287 (in Chinese) [17] Deng J,Li k,Irwin G W.Fast automatic two-stage nonlinear model identification based on the extreme learning machine[J].Neurocomputing,2011,74(16):2422-2429 [18] Wang Y G,Cao F L,Yuan Y B.A study on effectiveness of extreme learning machine[J].Neurocomputing,2011,74(16):2483-2490 [19] Heeswijk M V,Miche Y,Oja E,et al.GPU-accelerated and parallelized ELM ensembles for large-scale regression[J].Neurocomputing,2011,74(16):2430-2437 [20] 张贤达.矩阵分析与应用[M].北京:清华大学出版社,2005 Zhang Xianda.Matrix analysis and applications[M].Beijing:Tsinghua University Press,2005(in Chinese) [21] Time series prediction group. http://www.cis.hut. fi/projects/tsp/ page=Timeseries,2007
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出版历程
  • 收稿日期:  2010-07-23
  • 刊出日期:  2012-12-30

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