Research Article
Kalman Süzgeci Parametrelerinin Zeki Optimizasyon Algoritmalarıyla Eniyilenmesi Ve İstatiksel Analizi
Abstract
Rudolf Kalman tarafından geliştirilen iteratif yapılı Kalman Süzgeçleme
(KS), bir sonraki iterasyonda girdi olarak çıktıları kullanması ve hata
kovaryansını her iterasyon sürecinde azaltması prensibine göre çalışmaktadır.
Güncelleme ve tahmin temel iki adımından oluşan KS, sistemin kesitirilebilen
durumlarında her iki adımı da kullanırken, sistemde ölçülemeyen durumlarda
sadece tahmin adımını uygulamaktadır. KS parametrelerinden ölçüm kovaryans
matrisi (R), işlem kovaryans matrisi (Q) ve başlangıç hata kovaryansı (P)
değerlerinin uygun seçilmesi, sistemden daha doğru sonuçların elde edilmesini
sağlamaktadır. Klasik yaklaşımda kullanıcı deneyimine bırakılan bu
parametrelerin seçimi, zeki optimizasyon teknikleriyle eniyilenmektedir. Bu makalede,
KS parametrelerinden R, Q ve P parametreleri Genetik Algoritma (GA), Yapay Arı
Koloni Algoritması (YAKA), Diferansiyel Gelişim Algoritması (DGA), Parçacık
Sürüsü Optimizasyon Algoritması (PSOA) ve Ateş Böceği Algoritması (ABA)
kullanılarak eniyilenmiştir. Gerçekleştirilen benzetim çalışmaları ile
gürültülü voltaj okuma, eğik atış ve nesne takibi uygulamaları için farklı
algoritmaların farklı başarımlara sahip olduğu gözlemlenmiştir. Çalışmada
algoritma başarımlarına ait istatiksel analizler de sunulmuştur.
References
- Kalman, R.E., 1960. A New Approach to Linear Filtering and Prediction Problems, JournalofBasicEngineering,82(1):35.
- Yan, J., Yuan, D., Xing, X., and Jia, Q., 2008. Kalman filtering parameter optimization techniques based on genetic algorithm, 2008 IEEE International Conference on Automation and Logistics, Institute of Electrical & Electronics Engineers (IEEE).
- Ramakoti, N., Vinay, A., and Jatoth, R.K., 2009. Particle Swarm Optimization Aided Kalman Filter for Object Tracking, 2009 International Conference on AdvancesinComputing, Control, andTelecommunicationTechnologies,Institute of Electrical & Electronics Engineers (IEEE).
- Jatoth, R.K. and Kumar, T.K., 2009. Particle Swarm Optimization Based Tuning of Unscented Kalman Filter for Bearings Only Tracking, 2009 International Conference on Advances in Recent Technologies in Communication and Computing, Institute of Electrical & Electronics Engineers (IEEE)
- Jatoth, R.K. and Reddy, G.A., 2010. A Hybrid GA-Adaptive Particle Swarm Optimization Based Tuning of Unscented Kalman Filter for Harmonic Estimation, Swarm, Evolutionary, and Memetic Computing, 380–388, Springer Science Business Media.
- Jatoth, R.K., Rao, D.N., and Kumar, K.S., 2010. Particle Swarm Optimization aided unscented kalman filter for ballistic target tracking, 2010 International Conference On Communication Control And Computing Technologies, Institute of Electrical & Electronics Engineers (IEEE).
- Jin, Y., 2012. Application of Differential Evolution to the Parameter Optimization of the Unscented Kalman Filter, Communications in Computer and Information Science, 341–346, Springer Science Business Media.
- Ting, T.O., Man, K.L., Lim, E.G., and Leach, M., 2014. Tuning of Kalman Filter Parameters via Genetic Algorithm for State-of-Charge Estimation in Battery Management System,TheScientificWorldJournal,2014:1–11.
- Lin, G., Jing, Z., and Liu, Z., 2014. Tuning of Extended Kalman Filter using Improved Particle Swarm Optimization for Sensorless Control of Induction Motor,JournalofComputationalInformationSystems,2014:2455–2462.
- Laamari, Y., Chafaa, K., and Athamena, B., 2014. Particle swarm optimization of an extended Kalman filter for speed and rotor flux estimation of an induction motor drive,ElectrEng,97(2):129–138.
- Ozcan, T., and Basturk, A., 2016. Kalman Süzgeci Parametrelerinin Nesne Takibi Amacıyla Zeki Optimizasyon Algoritmalarıyla Belirlenmesi, 1st International Conferenceon Engineering Technology and Applied Sciences, 1384–1388, Afyon Kocatepe University.
- Ozcan, T., Badem ,H. , and Basturk, A., 2016, Artificial Bee Colony Algorithm Based Parameter Tuning of Kalman Filter for Object Tracking, International Conference on Information Complexity and Statistical Modeling in High Dimensions with Applications, Nevşehir
- Holland, J.H., 1992. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, A Bradford Book.
- Karaboga, D., 2011. Yapay Zeka Optimizasyon Algoritmaları, Nobel
- Storn, R. and Price, K., 1997. Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces, Journal of Global Optimization,11(4):341–359.
- Kennedy, J. and Eberhart, R., 1995. Particle swarm optimization, Proceedings of ICNN95-InternationalConferenceonNeuralNetworks,InstituteofElectrical& Electronics Engineers (IEEE).
- Yang, X.S., 2010. Nature-Inspired Metaheuristic Algorithms: Second Edition, Luniver Press.
Year 2016,
Volume: 1 Issue: 2, 18 - 35, 31.12.2016
Abstract
References
- Kalman, R.E., 1960. A New Approach to Linear Filtering and Prediction Problems, JournalofBasicEngineering,82(1):35.
- Yan, J., Yuan, D., Xing, X., and Jia, Q., 2008. Kalman filtering parameter optimization techniques based on genetic algorithm, 2008 IEEE International Conference on Automation and Logistics, Institute of Electrical & Electronics Engineers (IEEE).
- Ramakoti, N., Vinay, A., and Jatoth, R.K., 2009. Particle Swarm Optimization Aided Kalman Filter for Object Tracking, 2009 International Conference on AdvancesinComputing, Control, andTelecommunicationTechnologies,Institute of Electrical & Electronics Engineers (IEEE).
- Jatoth, R.K. and Kumar, T.K., 2009. Particle Swarm Optimization Based Tuning of Unscented Kalman Filter for Bearings Only Tracking, 2009 International Conference on Advances in Recent Technologies in Communication and Computing, Institute of Electrical & Electronics Engineers (IEEE)
- Jatoth, R.K. and Reddy, G.A., 2010. A Hybrid GA-Adaptive Particle Swarm Optimization Based Tuning of Unscented Kalman Filter for Harmonic Estimation, Swarm, Evolutionary, and Memetic Computing, 380–388, Springer Science Business Media.
- Jatoth, R.K., Rao, D.N., and Kumar, K.S., 2010. Particle Swarm Optimization aided unscented kalman filter for ballistic target tracking, 2010 International Conference On Communication Control And Computing Technologies, Institute of Electrical & Electronics Engineers (IEEE).
- Jin, Y., 2012. Application of Differential Evolution to the Parameter Optimization of the Unscented Kalman Filter, Communications in Computer and Information Science, 341–346, Springer Science Business Media.
- Ting, T.O., Man, K.L., Lim, E.G., and Leach, M., 2014. Tuning of Kalman Filter Parameters via Genetic Algorithm for State-of-Charge Estimation in Battery Management System,TheScientificWorldJournal,2014:1–11.
- Lin, G., Jing, Z., and Liu, Z., 2014. Tuning of Extended Kalman Filter using Improved Particle Swarm Optimization for Sensorless Control of Induction Motor,JournalofComputationalInformationSystems,2014:2455–2462.
- Laamari, Y., Chafaa, K., and Athamena, B., 2014. Particle swarm optimization of an extended Kalman filter for speed and rotor flux estimation of an induction motor drive,ElectrEng,97(2):129–138.
- Ozcan, T., and Basturk, A., 2016. Kalman Süzgeci Parametrelerinin Nesne Takibi Amacıyla Zeki Optimizasyon Algoritmalarıyla Belirlenmesi, 1st International Conferenceon Engineering Technology and Applied Sciences, 1384–1388, Afyon Kocatepe University.
- Ozcan, T., Badem ,H. , and Basturk, A., 2016, Artificial Bee Colony Algorithm Based Parameter Tuning of Kalman Filter for Object Tracking, International Conference on Information Complexity and Statistical Modeling in High Dimensions with Applications, Nevşehir
- Holland, J.H., 1992. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, A Bradford Book.
- Karaboga, D., 2011. Yapay Zeka Optimizasyon Algoritmaları, Nobel
- Storn, R. and Price, K., 1997. Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces, Journal of Global Optimization,11(4):341–359.
- Kennedy, J. and Eberhart, R., 1995. Particle swarm optimization, Proceedings of ICNN95-InternationalConferenceonNeuralNetworks,InstituteofElectrical& Electronics Engineers (IEEE).
- Yang, X.S., 2010. Nature-Inspired Metaheuristic Algorithms: Second Edition, Luniver Press.
There are 17 citations in total.
Details
| Subjects | Engineering |
|---|---|
| Journal Section | Research Articles |
| Authors | |
| Publication Date | December 31, 2016 |
| Submission Date | October 31, 2016 |
| Published in Issue | Year 2016 Volume: 1 Issue: 2 |
