<i>q</i> -affine Projection Algorithm and Its Steady-state Mean Square Convergence Analysis

Shiyuan WANG; Chunfen SHI; Yunxiang JIANG; Wenyue WANG; Guobing QIAN

doi:10.11999/JEIT171125

Volume 40 Issue 10

Sep. 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(10): 2402-2407

Shiyuan WANG, Chunfen SHI, Yunxiang JIANG, Wenyue WANG, Guobing QIAN. 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

Citation:

Shiyuan WANG, Chunfen SHI, Yunxiang JIANG, Wenyue WANG, Guobing QIAN. 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

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q -affine Projection Algorithm and Its Steady-state Mean Square Convergence Analysis

doi: 10.11999/JEIT171125

Shiyuan WANG^{1, 2
,
,},
Chunfen SHI^{1, 2},
Yunxiang JIANG³,
Wenyue WANG^{1, 2},
Guobing QIAN^{1, 2}

1.
College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China
2.
Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Chongqing 400715, China
3.
Graduate School at Shenzhen of Tsinghua University, Shenzhen 518055, China

Funds: The National Natural Science Foundation of China (61671389, 61701419), China Postdoctoral Science Foundation Funded Project (2017M610583, 2017M620783), Chongqing Postdoctoral Science Foundation Special Funded Project (Xm2017107, Xm2017104)

Received Date: 2017-11-28
Rev Recd Date: 2018-06-14

Available Online: 2018-07-30

Publish Date: 2018-10-01

Abstract

Abstract

The q-gradient is a generalized gradient based on the q-derivative concept. To improve the filtering performance of the Affine Projection Algorithm (APA), the q-gradient is applied to APA based on the minimum of the recent mean square errors, generating a novel q-Affine Projection Algorithm (q-APA). The q-APA with appropriate setting of q achieves desirable filtering performance in the presence of Gaussian noises. A sufficient condition for guaranteeing convergence of the proposed q-APA is also presented, and its steady-state Excess Mean Square Error (EMSE) of q-APA is obtained theoretically to evaluate the filtering performance. In addition, the Variable q-APA (V-q-APA) is developed to improve further the filtering performance. Simulations in the context of system identification demonstrate the superior filtering performance of the proposed algorithms compared with APA and Variable q-Least Mean Square (V-q-LMS) algorithm in the presence of Gaussian noise.
- Signal processing,
- q-derivative,
- Error criterion,
- Affine Projection Algorithm (APA),
- Mean square convergence analysis

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References(16)

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