LMS Uyarlamalı Filtrelerde Yakınsama Hızlandırma İçin Parçacık Sürü Optimizasyonu Kullanımı: Gerçek Zamanlı Sinyal İşleme İçin Hibrit Bir Yaklaşım

Yıl 2025, Cilt: 12 Sayı: 3, 311 - 328, 30.09.2025

Muhammed Davud

https://doi.org/10.31202/ecjse.1598491

Öz

Uyarlamalı filtreleme, özellikle En Küçük Ortalama Kareler (LMS) algoritmalarıyla, gürültü giderme, sistem tanımlama ve kontrol sistemleri gibi uygulamalarda temel bir rol oynamaktadır. Ancak, geleneksel LMS algoritmaları basitlikleri ve etkinliklerine rağmen yavaş yakınsama ve sayısal kararsızlık sorunlarıyla karşı karşıyadır. Bu makale, bu sınırlamaları aşmak için Parçacık Sürü Optimizasyonu'nu (PSO) ZA-LLMS, RZA-LLMS, ZA-VSS-LMS ve RZA-VSS-LMS gibi gelişmiş LMS türevleriyle birleştiren yenilikçi bir hibrit çerçeve sunmaktadır. PSO'nun ağırlık katsayılarını dinamik olarak optimize etme yeteneğinden yararlanarak önerilen algoritmalar, yakınsama hızını önemli ölçüde artırır ve ortalama kare hata (MSE) değerini azaltır, geleneksel yöntemlerin performansını geride bırakır. Ek Beyaz Gauss Gürültüsü (AWGN) ve Renkli Gauss Dizisi (CGS) gürültüsü kullanılarak yapılan deneysel değerlendirmeler, hibrit çerçevenin dayanıklılığını göstermekte ve iterasyon sayısında %67'ye varan bir azalma sağlamaktadır. Bu ilerleme, gerçek zamanlı sinyal işleme, telekomünikasyon sistemleri ve tıbbi tanı gibi yüksek hızlı uyarlamalı filtreleme gerektiren gerçek dünya uygulamaları için yeni fırsatlar sunmaktadır.

Anahtar Kelimeler

Uyarlamalı filtreleme , Hibrit Algoritma , LMS , Optimizasyon , Parçacık Sürü.

Accelerating Convergence in LMS Adaptive Filters Using Particle Swarm Optimization: A Hybrid Approach for Real-Time Signal Processing

Öz

Adaptive filtering, particularly with Least Mean Square (LMS) algorithms, is foundational in applications such as noise cancellation, system identification, and control systems. Despite their simplicity and effectiveness, traditional LMS algorithms are hindered by slow convergence and numerical instability. This paper introduces a novel hybrid framework that integrates Particle Swarm Optimization (PSO) with advanced LMS variants—including ZA-LLMS, RZA-LLMS, ZA-VSS-LMS, and RZA-VSS-LMS—to address these limitations. By leveraging PSO’s ability to optimize weight coefficients dynamically, the proposed algorithms significantly enhance convergence speed and reduce mean square error (MSE), outperforming traditional methods. Experimental evaluations using Additive White Gaussian Noise (AWGN) and Colored Gaussian Sequence (CGS) noise demonstrate the hybrid framework's robustness, achieving up to 67\% reduction in iterations. This advancement paves the way for real-world applications requiring high-speed adaptive filtering, such as real-time signal processing, telecommunication systems, and medical diagnostics.

Anahtar Kelimeler

Adaptive filtering , Hybrid Algorithm , LMS , Optimization , Particle Swarm.

Etik Beyan

This study adheres to the ethical principles and standards of research integrity. No human participants or animal subjects were involved in this study. All data used were obtained from publicly available sources or generated through simulations. The authors declare that there are no conflicts of interest, and all research was conducted following ethical guidelines. Proper citations and acknowledgments have been provided for all referenced works.

Kaynakça

• [1] M. E. sayed M. Sakr and M. A. M. Hassan, ‘‘Satellite tracking control system using optimal variable coefficients controllers based on evolutionary optimization techniques,’’ El-Cezeri Journal of Science and Engineering, vol. 10, no. 2, pp. 326–348, 2023.
• [2] M. K. Derdiman, ‘‘Ayrık pso algoritması ile sehim kısıtı altında İki doğrultudaki kirişli doşemelerin guvenilirlik tabanlı optimizasyonu,’’ El-Cezeri Journal of Science and Engineering, vol. 9, no. 1, pp. 49–64, 2022.
• [3] Ozdemir, S. "Ozturk, O. Şengul, and F. Kuncan, ‘‘Position control of the suspended pendulum system with particle swarm optimization algorithm,’’ El-Cezeri Journal of Science and Engineering, vol. 9, no. 2, pp. 669–679, 2022.
• [4] Y. Bai, X. Gong, Q. Lu, Y. Song, W. Zhu, S. Xue, D. Wang, Z. Peng, and Z. Zhang, ‘‘Application of adaptive filtering algorithm to the stability problem for double crystal monochromator. part i: Typical filtering algorithms,’’ Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, vol. 1048, p. 167924, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0168900222012165
• [5] R. Karthick, A. Senthilselvi, P. Meenalochini, and S. Senthil Pandi, ‘‘Design and analysis of linear phase finite impulse response filter using water strider optimization algorithm in fpga,’’ Circuits, Systems, and Signal Processing, vol. 41, no. 9, pp. 5254–5282, Sep 2022. [Online]. Available: https://doi.org/10.1007/s00034-022-02034-2
• [6] B. Durmuş, ‘‘Infinite impulse response system identification using average differential evolution algorithm with local search,’’ Neural Computing and Applications, vol. 34, no. 1, pp. 375–390, Jan 2022. [Online]. Available: https://doi.org/10.1007/s00521-021-06399-4
• [7] G. Clark, S. Mitra, and S. Parker, ‘‘Block implementation of adaptive digital filters,’’ IEEE Transactions on Circuits and Systems, vol. 28, no. 6, pp. 584–592, 1981.
• [8] Y. Yu, Z. Huang, H. He, Y. Zakharov, and R. C. de Lamare, ‘‘Sparsity-aware robust normalized subband adaptive filtering algorithms with alternating optimization of parameters,’’ IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 69, no. 9, pp. 3934–3938, 2022.
• [9] G. Boidi, M. R. Da Silva, F. J. Profito, and I. F. Machado, ‘‘Using machine learning radial basis function (rbf) method for predicting lubricated friction on textured and porous surfaces,’’ Surface Topography: Metrology and Properties, vol. 8, no. 4, p. 044002, 2020.
• [10] A. Singh, ‘‘Adaptive noise cancellation,’’ Dept. of Electronics & Communication, Netaji Subhas Institute of Technology, vol. 1, 2001.
• [11] H. Kolivand, K. A. Akintoye, S. Asadianfam, and M. S. Rahim, ‘‘Improved methods for finger vein identification using composite median-wiener filter and hierarchical centroid features extraction,’’ Multimedia Tools and Applications, pp. 1–32, 2023.
• [12] S. M. Wilson, M. K. Bohn, A. Madsen, T. Hundhausen, and K. Adeli, ‘‘Lms-based continuous reference percentiles for 14 laboratory parameters in the caliper cohort of healthy children and adolescents,’’ Clinical Chemistry and Laboratory Medicine (CCLM), 2023.
• [13] B. Singh, M. Kandpal, and I. Hussain, ‘‘Control of grid tied smart pv-dstatcom system using an adaptive technique,’’ IEEE transactions on smart grid, vol. 9, no. 5, pp. 3986–3993, 2016.
• [14] K. Mayyas and T. Aboulnasr, ‘‘Leaky lms algorithm: Mse analysis for gaussian data,’’ IEEE Transactions on Signal Processing, vol. 45, no. 4, pp. 927–934, 1997.
• [15] M. S. Salman, ‘‘Sparse leaky-lms algorithm for system identification and its convergence analysis,’’ International Journal of Adaptive Control and Signal Processing, vol. 28, no. 10, pp. 1065–1072, 2014. [Online]. Available: https://onlinelibrary.wiley.com/doi/abs/10.1002/acs.2428
• [16] Y. Li and M. Hamamura, ‘‘Zero-attracting variable-step-size least mean square algorithms for adaptive sparse channel estimation,’’ International Journal of Adaptive Control and Signal Processing, vol. 29, no. 9, pp. 1189–1206, 2015.
• [Online]. Available: https://onlinelibrary.wiley.com/doi/abs/10.1002/acs.2536
• [17] M. S. Salman, A. A. Hameed, C. Turan, and B. Karlik, ‘‘A new sparse convex combination of za-llms and rza-llms algorithms,’’ in 2015 23nd Signal Processing and Communications Applications Conference (SIU), 2015, pp. 711–714.
• [18] R. Kwong and E. Johnston, ‘‘A variable step size lms algorithm,’’ IEEE Transactions on Signal Processing, vol. 40, no. 7, pp. 1633–1642, 1992.
• [19] V. Mathews and Z. Xie, ‘‘A stochastic gradient adaptive filter with gradient adaptive step size,’’ IEEE Transactions on Signal Processing, vol. 41, no. 6, pp. 2075–2087, 1993.
• [20] T. Aboulnasr and K. Mayyas, ‘‘A robust variable step-size lms-type algorithm: analysis and simulations,’’ IEEE Transactions on Signal Processing, vol. 45, no. 3, pp. 631–639, 1997.
• [21] M. S. Salman, M. N. S. Jahromi, A. Hocanin, and O. Kukrer, ‘‘A zero-attracting variable step-size lms algorithm for sparse system identification,’’ in 2012 IX International Symposium on Telecommunications (BIHTEL), 2012, pp. 1–4.
• [22] W. Jia, S. Kong, T. Cai, M. Li, Y. Jin, P. Wang, and Z. Dai, ‘‘Steady-state performance analysis of the arctangent lms algorithm with gaussian input,’’ IEEE Transactions on Circuits and Systems II: Express Briefs, 2023.
• [23] H. Ferro, ‘‘A combination of adaptive filters based on competitive learning principles,’’ Available at SSRN 4345598, 2023.
• [24] Y. He, J. Wei, Y. He, X. Rong, W. Guo, F. Wang, Y. Wang, and J. Liu, ‘‘A process strategy planning of additivesubtractive hybrid manufacturing based multi-dimensional manufacturability evaluation of geometry feature,’’ Journal of Manufacturing Systems, vol. 67, pp. 296–314, 2023.
• [25] A. A. Vazquez, J. G. Avalos, G. Sanchez, J. C. Sanchez, and H. Perez, ‘‘A comparative survey of convex combination of adaptive filters,’’ IETE Journal of Research, vol. 69, no. 2, pp. 940–950, 2023.
• [26] M. Martinez-Ramon, J. Arenas-Garcia, A. Navia-Vazquez, and A. R. Figueiras-Vidal, ‘‘An adaptive combination of adaptive filters for plant identification,’’ in 2002 14th International Conference on Digital Signal Processing Proceedings. DSP 2002 (Cat. No. 02TH8628), vol. 2. IEEE, 2002, pp. 1195–1198.
• [27] R. C. Eberhart and Y. Shi, ‘‘Comparison between genetic algorithms and particle swarm optimization,’’ in Evolutionary Programming VII, V. W. Porto, N. Saravanan, D. Waagen, and A. E. Eiben, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998, pp. 611–616.
• [28] A. K. Mahapatra, N. Panda, and B. K. Pattanayak, ‘‘Hybrid PSO (SGPSO) with the incorporation of discretization operator for training RBF neural network and optimal feature selection,’’ Arabian Journal for Science and Engineering, vol. 48, no. 8, pp. 9991–10 019, Aug. 2023.
• [29] F. Yun, H. Dong, C. Liang, T. Weimin, and T. Chao, ‘‘Feature selection of XLPE cable condition diagnosis based on PSO-SVM,’’ Arabian Journal for Science and Engineering, vol. 48, no. 5, pp. 5953–5963, May 2023.
• [30] M. Saber, M. E. Ghoneim, and S. Kumar, ‘‘Survey on design of digital fir filters using optimization models,’’ Journal of Artificial Intelligence and Metaheuristics, vol. 2, no. 1, pp. 16–26, 2022.
• [31] D. Krusienski and W. Jenkins, ‘‘A particle swarm optimization-least mean squares algorithm for adaptive filtering,’’ in Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, 2004., vol. 1, 2004, pp. 241–245 Vol.1.
• [32] M. Chen, Y. Wang, P. Li, and H. Fu, ‘‘Research on an improved pso algorithm with dual self-adaptation and dual variation,’’ in 2022 IEEE International Conference on Mechatronics and Automation (ICMA), 2022, pp. 646–650.
• [33] J. Xin, G. Chen, and Y. Hai, ‘‘A particle swarm optimizer with multi-stage linearly-decreasing inertia weight,’’ in 2009 International Joint Conference on Computational Sciences and Optimization, vol. 1, 2009, pp. 505–508.
• [34] K. Deb, ‘‘Multi-objective optimisation using evolutionary algorithms: an introduction,’’ in Multi-objective evolutionary optimisation for product design and manufacturing. Springer, 2011, pp. 3–34.
• [35] B. Alhijawi and A. Awajan, ‘‘Genetic algorithms: Theory, genetic operators, solutions, and applications,’’ Evolutionary Intelligence, vol. 17, no. 3, pp. 1245–1256, 2024.
• [36] Q.-W. Chai, L. Kong, J.-S. Pan, and W.-M. Zheng, ‘‘A novel discrete artificial bee colony algorithm combined with adaptive filtering to extract fetal electrocardiogram signals,’’ Expert Systems with Applications, vol. 247, p. 123173, 2024.
• [37] J. C. Bansal, H. Sharma, and S. S. Jadon, ‘‘Artificial bee colony algorithm: a survey,’’ International Journal of Advanced Intelligence Paradigms, vol. 5, no. 1-2, pp. 123–159, 2013.
• [38] K. Price, R. M. Storn, and J. A. Lampinen, ‘‘Differential evolution: a practical approach to global optimization,’’ Springer Science & Business Media, 2006.
• [39] E. Reyes-Davila, E. H. Haro, A. Casas-Ordaz, D. Oliva, and O. Avalos, ‘‘Differential evolution: a survey on their operators and variants,’’ Archives of Computational Methods in Engineering, pp. 1–30, 2024.
• [40] H. Purwins, B. Li, T. Virtanen, J. Schluter, S.-Y. Chang, and T. Sainath, ‘‘Deep learning for audio signal processing,’’ IEEE Journal of Selected Topics in Signal Processing, vol. 13, no. 2, pp. 206–219, 2019.
• [41] A. Younesi, M. Ansari, M. Fazli, A. Ejlali, M. Shafique, and J. Henkel, ‘‘A comprehensive survey of convolutions in deep learning: Applications, challenges, and future trends,’’ IEEE Access, vol. 12, pp. 41 180–41 218, 2024.