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Heydari, E., Shams Esfand Abadi, M., Khademiyan, S.M.. (1402). The New Family of Adaptive Filter Algorithms for Block-Sparse System Identification. فناوری آموزش, 12(1), 133-146. doi: 10.22061/jecei.2023.10062.675
E. Heydari; M. Shams Esfand Abadi; S.M. Khademiyan. "The New Family of Adaptive Filter Algorithms for Block-Sparse System Identification". فناوری آموزش, 12, 1, 1402, 133-146. doi: 10.22061/jecei.2023.10062.675
Heydari, E., Shams Esfand Abadi, M., Khademiyan, S.M.. (1402). 'The New Family of Adaptive Filter Algorithms for Block-Sparse System Identification', فناوری آموزش, 12(1), pp. 133-146. doi: 10.22061/jecei.2023.10062.675
Heydari, E., Shams Esfand Abadi, M., Khademiyan, S.M.. The New Family of Adaptive Filter Algorithms for Block-Sparse System Identification. فناوری آموزش, 1402; 12(1): 133-146. doi: 10.22061/jecei.2023.10062.675

The New Family of Adaptive Filter Algorithms for Block-Sparse System Identification

Journal of Electrical and Computer Engineering Innovations (JECEI)
مقاله 9، دوره 12، شماره 1، فروردین 2024، صفحه 133-146    اصل مقاله (1.56 M)
نوع مقاله: Original Research Paper
شناسه دیجیتال (DOI): 10.22061/jecei.2023.10062.675
نویسندگان
E. Heydari1؛ M. Shams Esfand Abadi* 1؛ S.M. Khademiyan2
1Electrical Engineering Department, Faculty of Electrical Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran.
2Department of Mathematics, Faculty of Science, Shahid Rajaee Teacher Training University, Tehran, Iran
تاریخ دریافت: 31 تیر 1402،  تاریخ بازنگری: 25 شهریور 1402،  تاریخ پذیرش: 19 مهر 1402 
چکیده
Background and Objectives: In order to improve the performance of normalized subband adaptive filter algorithm (NSAF) for identifying the block-sparse (BS) systems, this paper introduces the novel adaptive algorithm which is called BSNSAF. In the following, an improved multiband structured subband adaptive filter (IMSAF) algorithms for BS system identification is also proposed. The BS-IMSAF has faster convergence speed than BS-NSAF. Since the computational complexity of BS-IMSAF is high, the selective regressor (SR) and dynamic selection (DS) approaches are utilized and BS-SR-IMSAF and BS-DS-IMSAF are introduced. Furthermore, the theoretical steady-state performance analysis of the presented algorithms is studied.
Methods: All algorithms are established based on the 𝐿2,0-norm constraint to the proposed cost function and the method of Lagrange multipliers is used to optimize the cost function.
Results: The good performance of the proposed algorithms is demonstrated through several simulation results in the system identification setup. The algorithms are justified and compared in various scenarios and optimum values of the parameters are obtained. Also, the computational complexity of different algorithms are studied. In addition, the theoretical steady state values of mean square error (MSE) values are compared with simulation values.
Conclusion: The BS-NSAF algorithm has better performance than NSAF for BS system identification. The BSIMSAF algorithm has better convergence speed than BS-NSAF. To reduce the computational complexity, the BS-SR-IMSAF and BS-DSR-IMSAF 
کلیدواژه‌ها
SparseL_2,0-norm IMSAF, Selective Regressors؛ Dynamic Selection
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