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Abstrakty
The aim of the paper is to present the possibilities of modeling the experimental data by Gaussian processes. Genetic algorithms are used for finding the Gaussian process parameters. Comparison of data modeling accuracy is made according to neural networks learned by Kalman filtering. Concrete hysteresis loops obtained by the experiment of cyclic loading are considered as the real data time series.
Słowa kluczowe
Rocznik
Tom
Strony
58--63
Opis fizyczny
Bibliogr. 31 poz., rys.
Twórcy
autor
- Faculty of Physics, Mathematics and Computer Science, Tadeusz Kościuszko Cracow University of Technology, Warszawska st 24, 31-155 Cracow, Poland
Bibliografia
- [1] J. D. Hamilton Time Series Analysis. Princeton University Press, 1994.
- [2] S. O. Haykin, Neural Networks and Learning Machines, 3rd ed. Prentice Hall, 2008.
- [3] C. E. Rasmussen and C. K. I. Williams, Gaussian Processes for Machine Learning. MIT Press, 2006.
- [4] Y. Kwon, K. Kim, K. J. Tompkin, J. H. Kim, and C. Theobalt, “Efficient learning of image super-resolution and compression artifact removal with semi-local Gaussian processes”, IEEE Trans. Pattern Anal. and Machine Intell., vol. PP, no. 99, 2014.
- [5] J. M. Young, “Probabilistic prediction of Alzheimer’s disease from multimodal image data with Gaussian processes”, Ph.D. thesis, University College London Press, 2015.
- [6] A. K. Manshada and H. Rostamib, “Prediction of Wax precipitation in crude oil systems using Gaussian processes”, Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, vol. 37, no. 1, pp. 84–91, 2015.
- [7] J. Nielsen and J. Larsen, “Perception-based personalization of hearing aids using Gaussian processes and active learning”, IEEE/ACM Trans. Speech, and Lang. Process., vol. 23, no. 1, pp. 162–173, 2015.
- [8] J. Diwale, S. Sanjay, L. Ioannis, and J. Colin, “Optimization of an airborne wind energy system using constrained Gaussian processes with transient measurements”, in Proc. Indian Control Conf. ICC 2015, Chennai (Madras), India, 2015.
- [9] M. Hamimid, S. M. Mimoune, and M. Feliachi, “Minor hysteresis loops model based on exponential parameters scaling of the modified Jiles–Atherton model”, Physica B: Condensed Matter, vol. 407, no. 13, pp. 2438–2441, 2012.
- [10] A. Ganczarski and L. Barwacz, “Low cycle fatigue based on unilateral damage evolution”, Int. J. of Damage Mechanics, vol. 16, no. 2, pp. 159–177, 2007.
- [11] K. Chwastek, “Modelling hysteresis loops in thick steel sheet with the dynamic Tak´acs model”, Physica B: Condensed Matter, vol. 407, no. 17, pp. 3632–3634, 2012.
- [12] M. Al Janaideh, “A time-dependent stop operator for modeling a class of singular hysteresis loops in a piezoceramic actuator”, Physica B: Condensed Matter, vol. 413, pp. 100–104, 2013.
- [13] A. P. S. Baghel, A. Gupta, K. Chwastek, and S. V. Kulkarni, “Comprehensive modelling of dynamic hysteresis loops in the rolling and transverse directions for transformer laminations”, Physica B: Condensed Matter, vol. 462, pp. 86–92, 2015.
- [14] I. Kucuk, “Prediction of hysteresis loop in magnetic cores using neural network and genetic algorithm”, J. Magnetism and Magnetic Materials, vol. 305, no. 2, pp. 423–427, 2006.
- [15] R. Dong, Y. Tan, H. Chen, and Y. Xie, “A neural networks based model for rate-dependent hysteresis for piezoceramic actuators”, Sensors and Actuators A: Physical, vol. 143, no. 2, pp. 370–376, 2008.
- [16] A. Nouicer, E. Nouicer, and F. Mouloudc, “A neural network for incorporating the thermal effect on the magnetic hysteresis of the 3F3 material using the Jiles–Atherton model”, J. Magnetism and Magnetic Materials, vol. 373, pp. 240–243, 2015.
- [17] V. Wolfs and P. Willems, “Development of discharge-stage curves affected by hysteresis using time varying models, model trees and neural networks”, Environ. Model. & Softw., vol. 55, pp. 107–119, 2014.
- [18] X. Zhang, Y. Tan, and M. Su, “Modeling of hysteresis in piezoelectric actuators using neural networks”, Mechan. Syst. and Sig. Process., vol. 23, no. 8, pp. 2699–2711, 2009.
- [19] A. Krok, “Analiza wybranych zagadnień mechaniki konstrukcji i materiałów za pomocą SSN i filtrow Kalmana (Analysis of mechanics of structures and material problems applying artifcial neural networks learnt by means of Kalman filtering)”, Ph.D. thesis, Tadeusz Kościuszko Cracow University of Technology, 2007 (in Polish).
- [20] D. J. C. MacKay, “Gaussian processes – a replacement for supervised neural networks?”, Lecture notes for a tutorial at Neural Information Processing Systems (NIPS) 1997, Cambridge University, 1997.
- [21] D. Whitley, T. Starkweather, and C. Bogart, “Genetic algorithms and neural networks: optimizing connections and connectivity”, Parallel Comput., vol. 14, no. 3, pp. 347–361, 1990.
- [22] D. Pham and D. Karaboga, Intelligent Optimisation Techniques: Genetic Algorithms, Tabu Search, Simulated Annealing and Neural Networks. Springer, 2011.
- [23] C. Bishop, Pattern Recognition and Machine Learning. Springer, 2006.
- [24] Ms. Dharmistha and D. Vishwakarma, “Genetic algorithm based weights optimization of artificial neural network”, Int. J. Adv. Res. Elec., Electron. and Instrumen. Engin., vol. 1, no. 3, 2012.
- [25] R. M. Neal, “Regression and classification using Gaussian process priors”, in Bayesian Statistics 6, J. M. Bernardo et al., Eds. Oxford University Press, 1998, pp. 475–501.
- [26] B. P. Sinha, K. H. Gerstle, and L. G. Tulin, “Stress-strain relations for concrete under cyclic loading”, J. of the American Concrete Institute, no. 61-12, 1964.
- [27] R. Neal, Software for Flexible Bayesian Modeling and Markov Chain Sampling [Online]. Available: http://http://www.cs.toronto.edu/∼radford/fbm.software.html
- [28] Optimization Toolbox User’s Guide – MathWorks, The MathWorks Inc. [Online]. Available: http://uk.mathworks.com/help/pdf-doc/optim/optim tb.pdf
- [29] S. N. Sivanandam and S. N. Deepa, Introduction to Genetic Algorithms. Springer, 2008.
- [30] Y. S. Othmana et al., “Frequency based hysteresis compensation for piezoelectric tube scanner using Artificial Neural Networks”, Procedia Engin., vol. 41, pp. 757–763, 2012.
- [31] F. Pernkopf and D. Bouchaffra, “Genetic-Based EM Algorithm for Learning Gaussian Mixture Models”, IEEE Trans. Pattern Anal. Machine Intell., vol. 27, no. 8, pp. 1344–1348, 2005.
Typ dokumentu
Bibliografia
Identyfikator YADDA
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