Optimizasyon Tabanlı FIR Süzgeç Tasarımlarında Performans Analizi

Yıl 2021, Sayı: 31, 8 - 22, 31.12.2021

Mehmet Fatih Karakaş , Fatma Latifoğlu

https://doi.org/10.31590/ejosat.958748

Cited By: 1

Öz

Dijital sinyal işleme sistemleri üzerinde tasarlanarak kullanılan dijital süzgeçlerin sonlu dürtü yanıtlı ve (FIR) ve sonsuz dürtü yanıtlı (IIR) olmak üzere iki çeşidi bulunmaktadır. FIR süzgeçlerin tasarımı esnasında kullanılan iki adet yaklaşım mevcuttur. Bunlardan ilki klasik yöntemler iken diğeri optimizasyon tabanlı yöntemlerdir. Optimizasyon tabanlı yöntemlerde metasezgisel algoritmaların kullanılması özellikle son yıllarda daha performanslı filtre tasarımları gerçekleştirebilmek adına giderek artmaktadır. İdeal süzgece olabildiğince yakın bir süzgeç tasarımı yapabilmek adına bu çalışmada çok amaçlı hata yaklaşımı kullanılarak metasezgisel algoritmalar ile FIR süzgeç tasarımları gerçekleştirilmiş ve tasarlanan bu süzgeçlere ait performans parametreleri analiz edilmiştir. FIR filtre katsayıları Genetik Algoritma, Parçacık Sürü Optimizasyonu Algoritması, Diferansiyel Gelişim Algoritması, Yapay Arı Kolonisi Algoritması, Karadul Örümceği Algoritması, Sincap Arama Algoritması ve Harmoni Arama Algoritmaları kullanılarak optimize edilmiştir. Elde edilen sonuçlar ve literatür çalışmalar karşılaştırıldığında önerilen yaklaşım ile geliştirilen FIR filtrenin performans parametrelerinde önemli iyileşmeler olduğu görülmüştür.

Anahtar Kelimeler

Metasezgisel Algoritmalar, FIR Filtre Tasarımı, Sinyal İşleme, Optimizasyon.

Performance Analysis in Optimization Based FIR Filter Designs

Öz

There are two types of digital filters designed and used on digital signal processing systems: finite impulse response (FIR) and infinite impulse response (IIR). There are two approaches used during the design of FIR filters. The first of these is classical methods, while the other is optimization-based methods. The use of metaheuristic algorithms in optimization-based methods has been increasing in recent years, especially in order to realize more performance filter designs. In order to design a filter as close to the ideal as possible, in this study, FIR filter designs were carried out with metaheuristic algorithms using the multi-objective error approach, and the performance parameters of these designed filters were analyzed. FIR filter coefficients were optimized using Genetic Algorithm, Particle Swarm Optimization Algorithm, Artificial Bee Colony Algorithm, Black Widow Algorithm, Squirrel Search Algorithm, and Harmony Search Algorithm. When the obtained results and literature studies were compared, it was seen that there were significant improvements in the performance parameters of the FIR filter obtained with the proposed approach.

Anahtar Kelimeler

Metaheuristic Algorithms, FIR Filter Design, Signal Processing, Optimization.

Kaynakça

• Aggarwal, A., Rawat, T. K. & Upadhyay, D. K. (2016). Design of optimal digital FIR filters using evolutionary and swarm optimization techniques. AEU - International Journal of Electronics and Communications, 70(4), 373–385. https://doi.org/10.1016/j.aeue.2015.12.012
• Bose, D., Biswas, S., Vasilakos, A. V. & Laha, S. (2014). Optimal filter design using an improved artificial bee colony algorithm. Information Sciences, 281, 443–461. https://doi.org/10.1016/j.ins.2014.05.033
• Chen, S. & Luk, B. L. (2010). Digital IIR filter design using particle swarm optimisation. International Journal of Modelling, Identification and Control, 9(4), 327–335.
• Clerc, M. & Kennedy, J. (2002). The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation, 6(1), 58–73.
• Geem, Z. W., Kim, J. H. & Loganathan, G. V. (2001). A new heuristic optimization algorithm: harmony search. Simulation, 76(2), 60–68.
• Gupta, L. & Mehra, R. (2011). Modified PSO based Adaptive IIR Filter Design for System Identification on FPGA. International Journal of Computer Applications, 22(5), 1–7. https://doi.org/10.5120/2583-3569
• Hayyolalam, V. & Pourhaji Kazem, A. A. (2020). Black Widow Optimization Algorithm: A novel meta-heuristic approach for solving engineering optimization problems. Engineering Applications of Artificial Intelligence, 87(July 2019), 103249. https://doi.org/10.1016/j.engappai.2019.103249
• Holland, J. H. & others. (1992). Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press.
• Jain, M., Singh, V. & Rani, A. (2019). A novel nature-inspired algorithm for optimization: Squirrel search algorithm. Swarm and Evolutionary Computation, 44(November 2017), 148–175. https://doi.org/10.1016/j.swevo.2018.02.013
• Jarraya, B. & Bouri, A. (2012). Metaheuristic Optimization Backgrounds: A Literature Review. International Journal of Contemporary Business Studies, 3(12), 2156–7506. http://www.akpinsight.webs.com
• Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization.
• Karaboga, N. (2005). Digital IIR filter design using differential evolution algorithm. Eurasip Journal on Applied Signal Processing, 2005(8), 1269–1276. https://doi.org/10.1155/ASP.2005.1269
• Karaboga, N. (2009). A new design method based on artificial bee colony algorithm for digital IIR filters. Journal of the Franklin Institute, 346(4), 328–348. https://doi.org/10.1016/j.jfranklin.2008.11.003
• Karaboga, N. & Cetinkaya, B. (2004). Design of minimum phase digital IIR filters by using genetic algorithm. Report - Helsinki University of Technology, Signal Processing Laboratory, 46, 29–32.
• Karaboga, N. & Cetinkaya, B. (2006). Design of digital FIR filters using differential evolution algorithm. Circuits, Systems, and Signal Processing, 25(5), 649–660. https://doi.org/10.1007/s00034-005-0721-7
• Karaboǧa, N. & Çetinkaya, M. B. (2011). A novel and efficient algorithm for adaptive filtering: Artificial bee colony algorithm. Turkish Journal of Electrical Engineering and Computer Sciences, 19(1), 175–190. https://doi.org/10.3906/elk-0912-344
• Karakaş, M. F. & Latifoğlu, F. (2020). Finite Impulse Response Filter Design Using Squirrel Search Algorithm. 2020 Medical Technologies Congress (TIPTEKNO), 1–4.
• Kaya, T. & İnce, M. C. (2011). Genetik Algoritma Yardımıyla Elde Edilen Yüksek Performa nslı Pencere Fonksiyonlarının Yinelemesiz Sayısal Filtre Tasarımında Kullanımı. May, 16–18.
• Kumar, A., Subhojit, D. & Londhe, N. D. (2017). Low-Power FIR Filter Design Using Hybrid Artificial Bee Colony Algorithm with Experimental Validation Over FPGA. Circuits, Systems, and Signal Processing, 36(1), 156–180. https://doi.org/10.1007/s00034-016-0297-4
• Latifoğlu, F. (2020). A novel singular spectrum analysis-based multi-objective approach for optimal FIR filter design using artificial bee colony algorithm. Neural Computing and Applications, 32(17), 13323–13341. https://doi.org/10.1007/s00521-019-04680-1
• Latifoǧlu, F. (2013). A novel approach to speckle noise filtering based on Artificial Bee Colony algorithm: An ultrasound image application. Computer Methods and Programs in Biomedicine, 111(3), 561–569. https://doi.org/10.1016/j.cmpb.2013.05.009
• Litwin, L. (2000). FIR and IIR digital filters. IEEE Potentials, 19(4), 28–31.
• Manuel, M. & Elias, E. (2012). Design of Sharp 2D Multiplier-Less Circularly Symmetric FIR Filter Using Harmony Search Algorithm and Frequency Transformation. Journal of Signal and Information Processing, 03(03), 344–351. https://doi.org/10.4236/jsip.2012.33044
• Nagasirisha, B. & Prasad, V. (2020). Noise Removal from EMG Signal Using Adaptive Enhanced Squirrel Search Algorithm. Fluctuation and Noise Letters, 19(04), 2050039.
• Najjarzadeh, M. & Ayatollahi, A. (2008). A comparison between genetic algorithm and PSO for linear phase fir digital filter design. International Conference on Signal Processing Proceedings, ICSP, 2134–2137. https://doi.org/10.1109/ICOSP.2008.4697568
• Oppenheim, A. V. (1999). Discrete-time signal processing. Pearson Education India.
• Parks, T. W. & Burrus, C. S. (1987). Digital filter design. Wiley-Interscience.
• Proakis, J. G. (2001). Digital signal processing: principles algorithms and applications. Pearson Education India.
• Reddy, K. S. & Sahoo, S. K. (2015). An approach for FIR filter coefficient optimization using differential evolution algorithm. AEU - International Journal of Electronics and Communications, 69(1), 101–108. https://doi.org/10.1016/j.aeue.2014.07.019
• Saha, S. K., Dutta, R., Choudhury, R., Kar, R., Mandal, D. & Ghoshal, S. P. (2013). Efficient and accurate optimal linear phase FIR filter design using opposition-based harmony search algorithm. The Scientific World Journal, 2013. https://doi.org/10.1155/2013/320489
• Saha, S. K., Kar, R., Mandal, D. & Ghoshal, S. P. (2014). Harmony search algorithm for infinite impulse response system identification. Computers and Electrical Engineering, 40(4), 1265–1285. https://doi.org/10.1016/j.compeleceng.2013.12.016
• Sarangi, S. K., Panda, R. & Abraham, A. (2020). Design of optimal low-pass filter by a new Levy swallow swarm algorithm. Soft Computing, 24(23), 18113–18128. https://doi.org/10.1007/s00500-020-05065-6
• Shao, P., Wu, Z., Zhou, X. & Tran, D. C. (2017). FIR digital filter design using improved particle swarm optimization based on refraction principle. In Soft Computing (Vol. 21, Issue 10, pp. 2631–2642). https://doi.org/10.1007/s00500-015-1963-3
• Storn, R. & Price, K. (1997). Differential evolution--a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4), 341–359.
• Zhang, G., Gu, Y., Hu, L. & Jin, W. (2003). A novel genetic algorithm and its application to digital filter design. IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 2, 1600–1605. https://doi.org/10.1109/ITSC.2003.1252754