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基于随机梯度的变动量因子自适应白化算法

欧世峰 高颖 赵晓晖

欧世峰, 高颖, 赵晓晖. 基于随机梯度的变动量因子自适应白化算法. 自动化学报, 2012, 38(8): 1370-1374. doi: 10.3724/SP.J.1004.2012.01370
引用本文: 欧世峰, 高颖, 赵晓晖. 基于随机梯度的变动量因子自适应白化算法. 自动化学报, 2012, 38(8): 1370-1374. doi: 10.3724/SP.J.1004.2012.01370
OU Shi-Feng, GAO Ying, ZHAO Xiao-Hui. Stochastic Gradient Based Variable Momentum Factor Algorithm for Adaptive Whitening. ACTA AUTOMATICA SINICA, 2012, 38(8): 1370-1374. doi: 10.3724/SP.J.1004.2012.01370
Citation: OU Shi-Feng, GAO Ying, ZHAO Xiao-Hui. Stochastic Gradient Based Variable Momentum Factor Algorithm for Adaptive Whitening. ACTA AUTOMATICA SINICA, 2012, 38(8): 1370-1374. doi: 10.3724/SP.J.1004.2012.01370

基于随机梯度的变动量因子自适应白化算法

doi: 10.3724/SP.J.1004.2012.01370
详细信息
    通讯作者:

    高颖

Stochastic Gradient Based Variable Momentum Factor Algorithm for Adaptive Whitening

  • 摘要: 针对自适应白化技术中算法的收敛速度问题, 通过融入具有变动量因子特性的动量项,提出了一种快速的自适应白化算法. 该算法利用动量项来加速系统的收敛速度,并基于随机梯度方法对动量因子进行自适应更新,有效提升了白化系统的整体性能. 仿真实验表明本文算法在平稳和非平稳环境下具有良好的性能.
  • [1] Cichocki A, Amari S. Adaptive Blind Signal and Image Processing: Learning Algorithms and Application. New York: John Wiley and Sons, 2002. 129-175[2] Choi S, Cichocki A, Park H M, Lee S Y. Blind source separation and independent component analysis: a review. Neural Information Processing —Letters and Reviews, 2005, 6(1): 1-57[3] Tang Ying, Li Jian-Ping. A new algorithm of ICA: using the parametrized orthogonal matrixes of any dimensions. Acta Automatica Sinica, 2008, 34(1): 31-39(汤影, 李建平. 利用参数表示任意维数正交矩阵的ICA新算法. 自动化学报, 2008, 34(1): 31-39)[4] Feng D Z, Zheng W X, Cichochi A. Matrix-group algorithm via improved whitening process for extracting statistically independent sources from array signals. IEEE Transactions on Signal Processing, 2007, 55(3): 962-977[5] Mollah M N, Eguchi S, Minami M. Robust prewhitening for ICA by minimizing β -divergence and its application to fastICA. Neural Processing Letters, 2007, 25(2): 91-110[6] Kocinski J, Libiszewski P, Sek A. Spatial efficiency of blind source separation based on decorrelation —subjective and objective assessment. Speech Communication, 2011, 53(3): 390-402[7] Coviello C M, Yoon P A, Sibul L H. Source separation and tracking for time-varying systems. IEEE Transactions on Aerospace and Electronic Systems, 2008, 44(3): 1198-1214[8] Douglas S C, Cichocki A. Adaptive step size techniques for decorrelation and blind source separation. In: Proceedings of the 32th Asilomar Conference on Signals, Systems, and Computers. Pacific Grove, USA: IEEE, 1998. 1191-1195[9] Ou S F, Zhao X H, Gao Y. Variable step size technique for adaptive blind decorrelation. In: Proceedings of the 8th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing. Qingdao, China: IEEE, 2007. 823-826[10] Yuan L X, Wang W W, Chambers J A. Variable step-size sign natural gradient algorithm for sequential blind source separation. IEEE Signal Processing Letters, 2005, 12(8): 589-592[11] Hsieh S T, Sun T Y, Lin C L, Liu C C. Effective learning rate adjustment of blind source separation based on an improved particle swarm optimizer. IEEE Transactions on Evolutionary Computation, 2008, 12(2): 242-251[12] Arenas-Garcia J, Gomez-Verdejo V, Figueiras-Vidal A R. New algorithms for improved adaptive convex combination of LMS transversal filters. IEEE Transactions on Instrumentation and Measurement, 2005, 54(6): 2239-2249[13] Mkadem F, Boumaiza S. Physically inspired neural network model for RF power amplifier behavioral modeling and digital predistortion. IEEE Transactions on Microwave Theory and Techniques, 2011, 59(4): 913-923[14] Zhang Z, Cui C, Liu K. Iterative hard-threshold algorithm with momentum. Electronics Letters, 2011, 47(4): 257-259[15] Xia C L, Guo C, Shi T N. A neural-network-identifier and fuzzy-controller-based algorithm for dynamic decoupling control of permanent-magnet spherical motor. IEEE Transactions on Industrial Electronics, 2010, 57(8): 2868-2878[16] Craven B D, Mond B. Linear programming with matrix variables. Linear Algebra and Its Applications, 1981, 38: 73-80[17] Qian N. On the momentum term in gradient descent learning algorithms. Neural Networks, 1999, 12(1): 145-151
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出版历程
  • 收稿日期:  2011-03-31
  • 修回日期:  2011-10-18
  • 刊出日期:  2012-08-20

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