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一种有效的储备池在线稀疏学习算法

韩敏 王新迎

韩敏, 王新迎. 一种有效的储备池在线稀疏学习算法. 自动化学报, 2011, 37(12): 1536-1540. doi: 10.3724/SP.J.1004.2011.01536
引用本文: 韩敏, 王新迎. 一种有效的储备池在线稀疏学习算法. 自动化学报, 2011, 37(12): 1536-1540. doi: 10.3724/SP.J.1004.2011.01536
HAN Min, WANG Xin-Ying. An Effective Online Sparse Learning Algorithm for Echo State Networks. ACTA AUTOMATICA SINICA, 2011, 37(12): 1536-1540. doi: 10.3724/SP.J.1004.2011.01536
Citation: HAN Min, WANG Xin-Ying. An Effective Online Sparse Learning Algorithm for Echo State Networks. ACTA AUTOMATICA SINICA, 2011, 37(12): 1536-1540. doi: 10.3724/SP.J.1004.2011.01536

一种有效的储备池在线稀疏学习算法

doi: 10.3724/SP.J.1004.2011.01536
详细信息
    通讯作者:

    王新迎 大连理工大学电子信息与电气工程学部博士研究生.主要研究方向为神经网络,时间序列预测. E-mail: xinying@mail.dlut.edu.cn

An Effective Online Sparse Learning Algorithm for Echo State Networks

  • 摘要: 为克服传统储备池方法缺乏良好在线学习算法的问题, 同时考虑到储备池本身存在的不适定问题, 本文提出一种储备池在线稀疏学习算法, 对储备池目标函数施加L1正则化约束,并采用截断梯度算法在线近似求解.所提算法在对储备池输出权值进行在线调整的同时, 可对储备池输出权值的稀疏性进行有效控制, 有效保证了网络的泛化性能.理论分析和仿真实例证明所提算法的有效性.
  • [1] Jaeger H, Haas H. Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication. Science, 2004, 304(5667): 78-80[2] Maass W, Natschlager T, Markram H. Real-time computing without stable states: a new framework for neural computation based on perturbations. Neural Computation, 2002, 14(11): 2531-2560[3] Han Min, Wang Ya-Nan. Multivariate time series online predictor with Kalman filter trained reservoir. Acta Automatica Sinica, 2010, 36(1): 169-173 (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)[4] Jaeger H, Maass W, Principe J. Special issue on echo state networks and liquid state machines. Neural Networks, 2007, 20(3): 287-289[5] Liu Ying, Zhao Jun, Wang Wei, Wu Yi-Ping, Chen Wei-Chang. Improved echo state network based on data-driven and its application to prediction of blast furnace gas output. Acta Automatica Sinica, 2009, 35(6): 731-738 (刘颖, 赵珺, 王伟, 吴毅平,陈伟昌. 基于数据的改进回声状态网络在高炉煤气发生量预测中的应用. 自动化学报, 2009, 35(6): 731-738)[6] Roseschies B, Igel C. Structure optimization of reservoir networks. Logic Journal of the IGPL, 2010, 18(5): 635-669[7] 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[8] Steil J J. Online stability of backpropagation-decorrela-tion recurrent learning. Neurocomputing, 2006,69(7-9): 642-650[9] Jaeger H. Reservoir riddles: suggestions for echo state network research. In: Proceedings of the IEEE International Joint Conference on Neural Networks. Montreal, Canada: IEEE, 2005. 1460-1462[10] Tibshirani R. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society Series B (Methodological), 1996, 58(1): 267-288[11] Shevade S K, Keerthi S S. A simple and efficient algorithm for gene selection using sparse logistic regression. Bioinformatics, 2003, 19(17): 2246-2253[12] Langford J, Li L, Zhang T. Sparse online learning via truncated gradient. The Journal of Machine Learning Research, 2009,10: 777-801[13] Donoho D L. For most large underdetermined systems of linear equations the minimal L1-norm solution is also the sparsest solution. Communications on Pure and Applied Mathematics, 2006,59(6): 797-829
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出版历程
  • 收稿日期:  2011-02-24
  • 修回日期:  2011-07-07
  • 刊出日期:  2011-12-20

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