| Citation: | Zhijin ZHAO, Sijia CHEN. A Kernel Normalization Decorrelated Affine Projection P-norm Algorithm Based on Gaussian Kernel Explicit Mapping[J]. Journal of Electronics & Information Technology, 2020, 42(8): 1896-1901. doi: 10.11999/JEIT190602
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In order to reduce the computation complexity and storage capacity of the Kernel Affine Projection P-norm (KAPP) algorithm, and improve the convergence rate and steady-state performance of the algorithm when the input signal is strongly correlated, a Kernel Normalization Decorrelated Affine Projection P-norm algorithm based on Gaussian Kernel Explicit Mapping (KNDAPP-GKEM) is proposed. The correlation of the input signal is eliminated in advance by the normalized correlation method. The explicit kernel function is approximated by Gaussian kernel explicit mapping method, which eliminates the dependence on historical data and solves the problem that the computation and storage capacity of the KAPP algorithm are too high due to the continuous growth of structure. The simulation results of nonlinear system identification under α-stable distribution noise environment show that when the training data scale is large, the KNDAPP-GKEM algorithm still maintains a fast convergence rate and the low identification mean square error of nonlinear system. Moreover, its training time is linearly and slowly increased, which is more conducive to the practical application of nonlinear system identification.
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OZEKI K and UMEDA T. An adaptive filtering algorithm using an orthogonal projection to an affine subspace and its properties[J]. Electronics and Communications in Japan, 1984, 67(5): 19–27. doi: 10.1002/ecja.4400670503
|
|
王世元, 史春芬, 蒋云翔, 等. 基于q梯度的仿射投影算法及其稳态均方收敛分析[J]. 电子与信息学报, 2018, 40(10): 2402–2407. doi: 10.11999/JEIT171125
WANG Shiyuan, SHI Chunfen, JIANG Yunxiang, et al. Q-affine projection algorithm and its steady-state mean square convergence analysis[J]. Journal of Electronics &Information Technology, 2018, 40(10): 2402–2407. doi: 10.11999/JEIT171125
|
|
王兰, 杨育红, 李良山. 解相关变阶仿射投影窄带干扰抑制算法[J]. 信息工程大学学报, 2016, 17(3): 266–269, 280. doi: 10.3969/j.issn.1671-0673.2016.03.003
WANG Lan, YANG Yuhong, and LI Liangshan. Decorrelating affine projection algorithm with variable order for narrowband interference suppression[J]. Journal of Information Engineering University, 2016, 17(3): 266–269, 280. doi: 10.3969/j.issn.1671-0673.2016.03.003
|
|
LIU Weifeng, PRÍNCIPE J C, and HAYKIN S. Kernel Adaptive Filtering: A Comprehensive Introduction[M]. Hoboken, USA: Wiley, 2010: 69–93.
|
|
李群生, 赵剡, 寇磊, 等. 一种基于多尺度核学习的仿射投影滤波算法[J]. 电子与信息学报, 2020, 42(4): 924–931. doi: 10.11999/JEIT190023
LI Qunsheng, ZHAO Yan, KOU Lei, et al. An affine projection algorithm with multi-scale kernels learning[J]. Journal of Electronics &Information Technology, 2020, 42(4): 924–931. doi: 10.11999/JEIT190023
|
|
邱天爽, 张旭秀, 李小兵, 等. 统计信号处理: 非高斯信号处理及其应用[M]. 北京: 电子工业出版社, 2004: 131–171.
QIU Tianshuang, ZHANG Xuxiu, LI Xiaobin, et al. Statistical Signal Processing: Non-Gauss Signal Processing and Its Application[M]. Beijing: Electronics Industry Press, 2004: 131–171.
|
|
金明明. 核自适应滤波算法研究[D]. [硕士论文], 杭州电子科技大学, 2017: 48–54.
JIN Mingming. The research on kernel adaptive filtering algorithms[D]. [Master dissertation], Hangzhou Dianzi University, 2017: 48–54.
|
|
刘勇, 江沙里, 廖士中. 基于近似高斯核显式描述的大规模SVM求解[J]. 计算机研究与发展, 2014, 51(10): 2171–2177. doi: 10.7544/issn1000-1239.2014.20130825
LIU Yong, JIANG Shali, and LIAO Shizhong. Approximate gaussian kernel for large-scale SVM[J]. Journal of Computer Research and Development, 2014, 51(10): 2171–2177. doi: 10.7544/issn1000-1239.2014.20130825
|
|
RAHIMI A and RECHT B. Uniform approximation of functions with random bases[C]. Proceedings of the 46th Annual Allerton Conference on Communication, Control, and Computing, Urbana-Champaign, USA, 2008: 555–561. doi: 10.1109/ALLERTON.2008.4797607.
|
|
BOROUMAND M and FRIDRICH J. Applications of explicit non-linear feature maps in steganalysis[J]. IEEE Transactions on Information Forensics and Security, 2018, 13(4): 823–833. doi: 10.1109/TIFS.2017.2766580
|
|
HU Zhen, LIN Ming, and ZHANG Changshui. Dependent online kernel learning with constant number of random fourier features[J]. IEEE Transactions on Neural Networks and Learning Systems, 2015, 26(10): 2464–2476. doi: 10.1109/TNNLS.2014.2387313
|
|
SHARMA M, JAYADEVA, SOMAN S, et al. Large-scale minimal complexity machines using explicit feature maps[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2017, 47(10): 2653–2662. doi: 10.1109/TSMC.2017.2694321
|
|
王迎旭. 基于随机特征的多核分布式协同模糊聚类算法研究[D]. [硕士论文], 济南大学, 2019: 21–65.
WANG Yingxu. Research of random feature based multiple kernel collaborative fuzzy clustering method in P2P distributed network[D]. [Master dissertation], University of Jinan, 2019: 21–65.
|
|
LIU Yuqi, SUN Chao, and JIANG Shouda. A kernel least mean square algorithm based on randomized feature networks[J]. Applied Sciences, 2018, 8(3): 458. doi: 10.3390/app8030458
|
|
王永德, 王军. 随机信号分析基础[M]. 3版. 北京: 电子工业出版社, 2009: 11.
WANG Yongde and WANG Jun. Fundamentals of Random Signal Analysis[M]. 3rd ed. Beijing: Electronic Industry Press, 2009: 11.
|
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