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

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.

Kaynakça

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