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Parçacık Filtresinin Optimizasyonu için Genetik Algoritma Tabanlı Yeni Bir Yaklaşım

Year 2023, Volume: 2 Issue: 1, 24 - 33, 21.06.2023

Abstract

Parçacık filtresi algoritması, Bayes tahmin teorisi çerçevesinde Monte Carlo fikrini kullanan bir filtreleme yöntemidir. Parçacıkları ve ağırlıklarından oluşan ayrık rasgele ölçüyü kullanarak olasılık dağılımına yaklaşır, yeni ayrık rasgele ölçüyü algoritmaya göre yinelemeli olarak günceller. Parçacık filtresi (PF) algoritması, doğrusal olmayan Gauss olmayan herhangi bir sisteme durum tahmini için uygulanabilir ve son on yılda birçok mühendislik alanında büyük ilgi görmüştür. Ancak standart parçacık filtresi mevcut ölçülen değeri dikkate almaz, bu da bazı iterasyonlardan sonra birkaç parçacık dışında kalan parçacıkların ağırlığının neredeyse yok denecek kadar az olmasına sebep olur. Böylelikle parçacık bozulması sorunu ortaya çıkar. Bozulmayı önlemek için PF yeniden örnekleme tekniğini kullanır, fakat bu parçacık çeşitliliğini azaltır ve parçacık yoksullaşmasına neden olur. Bu makalede, filtrenin bu sorununun üstesinden gelebilmek için Parçacık filtresine Genetik algoritma (GA) dahil edilmiştir. Genetik algoritmadaki seçim, çaprazlama ve mutasyon operatörlerinin özellikleriyle birleştirilmiş geliştirilmiş bir parçacık filtresi önerilmiştir. PF algoritmasının yeniden örnekleme aşamasından önce, en yüksek ağırlığa sahip parçacıklar bir genetik algoritma kullanılarak evrimleştirilir. Önerilen algoritmada, GA tarafından optimize edilen parçacık kümesi hedefin gerçek durumunu daha iyi ifade eder ve anlamlı parçacıkların sayısı önemli ölçüde artmıştır. Son olarak, önerilen yöntemin etkinliğini göstermek için bir bilgisayar simülasyonu gerçekleştirilmiştir. Simülasyon sonuçları, önerilen GA tabanlı yeni algoritmanın tahmin doğruluğunu standart parçacık filtresine kıyasla önemli ölçüde iyileştirdiğini göstermektedir.

References

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  • [24] Kwok, Ngai Ming, Gu Fang, and Weizhen Zhou. “Evolutionary particle filter: re-sampling from the genetic algorithm perspective ”. 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 2005.
  • [25] Doucet, A., Andrieu, C., & Fitzgerald, W. (1998, September). Bayesian filtering for hidden Markov models via Monte Carlo methods. In Neural Networks for Signal Processing VIII. Proceedings of the 1998 IEEE Signal Processing Society Workshop (Cat. No. 98TH8378) (pp. 194-203). IEEE.
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Year 2023, Volume: 2 Issue: 1, 24 - 33, 21.06.2023

Abstract

References

  • [1] Kalman, R. E. “A new approach to linear filtering and prediction problems”. 1960.
  • [2] Ristic, B., Arulampalam, S., & Gordon, N. “Beyond the Kalman filter: Particle filters for tracking applications”. Artech house. 2003.
  • [3] Arulampalam, M. S., Maskell, S., Gordon, N., & Clapp, T. “A tutorial on particle filters for online nonlinear/nongaussian bayesian tracking”. Bayesian Bounds Param. Estim. Nonlinear Filter. Track, 50, 723-737. 2007.
  • [4] Gordon, N. J., Salmond, D. J., & Smith, A. F. “Novel approach to nonlinear/non-Gaussian Bayesian state estimation”. In IEE proceedings F (radar and signal processing) (Vol. 140, No. 2, pp. 107-113). IET Digital Library. 1993, April
  • [5] Yang, J., Cui, X., Li, J., Li, S., Liu, J., & Chen, H., “Particle filter algorithm optimized by genetic algorithm combined with particle swarm optimization”. Procedia Computer Science, 187, 206-211. 2021.
  • [6] Gustafsson, F., Gunnarsson, F., Bergman, N., Forssell, U., Jansson, J., Karlsson, R., & Nordlund, P. J. “Particle filters for positioning, navigation, and tracking”. IEEE Transactions on signal processing, 50(2), 425-437. 2002.
  • [7] Shenoy, A. V., Prakash, J., Prasad, V., Shah, S. L., & McAuley, K. B. “Practical issues in state estimation using particle filters: Case studies with polymer reactors”. Journal of Process Control, 23(2), 120-131. 2013.
  • [8] Das, S., Kale, A., & Vaswani, N. “Particle filter with a mode tracker for visual tracking across illumination changes”. IEEE Transactions on Image Processing, 21(4), 2340-2346. 2011.
  • [9] Zhang, Q. B., Wang, P., & Chen, Z. H., “An improved particle filter for mobile robot localization based on particle swarm optimization. Expert Systems with Applications”, 135, 181-193. 2019.
  • [10] Li, M., Pang, B., He, Y., & Nian, F. “Particle Filter Improved by Genetic Algorithm and Particle Swarm Optimization Algorithm”. J. Softw., 8(3), 666-672. 2013.
  • [11] Park, S., Hwang, J. P., Kim, E., & Kang, H. J. “A new evolutionary particle filter for the prevention of sample impoverishment”. IEEE Transactions on Evolutionary Computation, 13(4), 801-809. 2009.
  • [12] Wang, W., Tan, Q. K., Chen, J., & Ren, Z. “Particle filter based on improved genetic algorithm resampling”. In 2016 IEEE Chinese Guidance, Navigation and Control Conference (CGNCC) (pp. 346-350). IEEE. August, 2016.
  • [13] Zhao, B., Hu, J. W., & Ji, B. “An improved particle filter based on genetic resampling”. In 2015 International Conference on Automation, Mechanical Control and Computational Engineering (pp. 1353-1358). Atlantis Press. April, 2015.
  • [14] Han, H., Ding, Y. S., Hao, K. R., & Liang, X. “An evolutionary particle filter with the immune genetic algorithm for intelligent video target tracking”. Computers & Mathematics with Applications, 62(7), 2685-2695. 2011.
  • [15] Walia, G. S., & Kapoor, R. “Intelligent video target tracking using an evolutionary particle filter based upon improved cuckoo search”. Expert Systems with Applications, 41(14), 6315-6326. 2014.
  • [16] J. P. Zhong, Y. F. Fung, “A biological inspired improvement strategy for particle filters,” Industrial Technology, 2009, ICIT 2009, pp. 1-6, 10-13, Feb. 2009.
  • [17] Y. Qiao, Q. Zhang and J. Zhang, “A fault predication algorithm based on artificial immune particle filter,” Applied Mechanics and Materials, vol. 44-47, pp. 3459-3463, 2010.
  • [18] X. Liang, J. Feng, Q. Li, T. Lu and B. Li, “A swarm intelligence optimization for particle filter,” Intelligent Control and Automation, 2008. WCICA 2008., pp. 1986-1991, 25-27, June 2008.
  • [19] Gao, M. L., Li, L. L., Sun, X. M., Yin, L. J., Li, H. T., & Luo, D. S. “Firefly algorithm (FA) based particle filter method for visual tracking”. Optik, 126(18), 1705-1711. 2015
  • [20] Dilmen, H. & Talu, M. F. “Yapısal Özellikleri Kullanan Parçacık Filtresi İle Uzun Süreli Nesne Takibi”. Gazi University Journal of Science Part C: Design and Technology, 5 (1), 107-118. Retrieved from https://dergipark.org.tr/tr/pub/gujsc/issue/28467/303419, 2017.
  • [21] Sadegh Moghadasi, S., & Faraji, N. “An efficient target tracking algorithm based on particle filter and genetic algorithm”. International Journal of Engineering, 32(7), 915-923. 2019.
  • [22] Gao, M., & Zhang, H. “Sequential Monte Carlo methods for parameter estimation in nonlinear state-space models”. Computers & Geosciences, 44, 70-77. 2012.
  • [23] Zhang, J., Pan, T. S., & Pan, J. S. “A parallel hybrid evolutionary particle filter for nonlinear state estimation”. In 2011 First International Conference on Robot, Vision and Signal Processing (pp. 308-312). IEEE. November, 2011.
  • [24] Kwok, Ngai Ming, Gu Fang, and Weizhen Zhou. “Evolutionary particle filter: re-sampling from the genetic algorithm perspective ”. 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 2005.
  • [25] Doucet, A., Andrieu, C., & Fitzgerald, W. (1998, September). Bayesian filtering for hidden Markov models via Monte Carlo methods. In Neural Networks for Signal Processing VIII. Proceedings of the 1998 IEEE Signal Processing Society Workshop (Cat. No. 98TH8378) (pp. 194-203). IEEE.
  • [26] Hodson, T. O. “Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not”. Geoscientific Model Development, 15(14), 5481-5487. 2022.
There are 26 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Research Articles
Authors

Fatma Selcen Turgun 0000-0003-4372-7026

Hasan Zorlu 0000-0001-8173-6228

Publication Date June 21, 2023
Published in Issue Year 2023 Volume: 2 Issue: 1

Cite

APA Turgun, F. S., & Zorlu, H. (2023). Parçacık Filtresinin Optimizasyonu için Genetik Algoritma Tabanlı Yeni Bir Yaklaşım. Bozok Journal of Engineering and Architecture, 2(1), 24-33.
AMA Turgun FS, Zorlu H. Parçacık Filtresinin Optimizasyonu için Genetik Algoritma Tabanlı Yeni Bir Yaklaşım. BJEA. June 2023;2(1):24-33.
Chicago Turgun, Fatma Selcen, and Hasan Zorlu. “Parçacık Filtresinin Optimizasyonu için Genetik Algoritma Tabanlı Yeni Bir Yaklaşım”. Bozok Journal of Engineering and Architecture 2, no. 1 (June 2023): 24-33.
EndNote Turgun FS, Zorlu H (June 1, 2023) Parçacık Filtresinin Optimizasyonu için Genetik Algoritma Tabanlı Yeni Bir Yaklaşım. Bozok Journal of Engineering and Architecture 2 1 24–33.
IEEE F. S. Turgun and H. Zorlu, “Parçacık Filtresinin Optimizasyonu için Genetik Algoritma Tabanlı Yeni Bir Yaklaşım”, BJEA, vol. 2, no. 1, pp. 24–33, 2023.
ISNAD Turgun, Fatma Selcen - Zorlu, Hasan. “Parçacık Filtresinin Optimizasyonu için Genetik Algoritma Tabanlı Yeni Bir Yaklaşım”. Bozok Journal of Engineering and Architecture 2/1 (June 2023), 24-33.
JAMA Turgun FS, Zorlu H. Parçacık Filtresinin Optimizasyonu için Genetik Algoritma Tabanlı Yeni Bir Yaklaşım. BJEA. 2023;2:24–33.
MLA Turgun, Fatma Selcen and Hasan Zorlu. “Parçacık Filtresinin Optimizasyonu için Genetik Algoritma Tabanlı Yeni Bir Yaklaşım”. Bozok Journal of Engineering and Architecture, vol. 2, no. 1, 2023, pp. 24-33.
Vancouver Turgun FS, Zorlu H. Parçacık Filtresinin Optimizasyonu için Genetik Algoritma Tabanlı Yeni Bir Yaklaşım. BJEA. 2023;2(1):24-33.