The Use of Genetic Algorithms for Searching Parameter Space in Gaussian Process Modeling
DOI:
https://doi.org/10.26636/jtit.2015.3.970Keywords:
Gaussian processes, genetic algorithmsAbstract
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.
Downloads
References
[1] J. D. Hamilton, Time Series Analysis. Princeton University Press, 1994.
View in Google Scholar
[2] S. O. Haykin, Neural Networks and Learning Machines, 3rd ed. Prentice Hall, 2008.
View in Google Scholar
[3] C. E. Rasmussen and C. K. I. Williams, Gaussian Processes for Machine Learning. MIT Press, 2006.
View in Google Scholar
[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.
View in Google Scholar
[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.
View in Google Scholar
[6] A. K. Manshad and H. Rostami, “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.
View in Google Scholar
[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.
View in Google Scholar
[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.
View in Google Scholar
[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.
View in Google Scholar
[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.
View in Google Scholar
[11] K. Chwastek, “Modelling hysteresis loops in thick steel sheet with the dynamic Takács model”, Physica B: Condensed Matter, vol. 407, no. 17, pp. 3632–3634, 2012.
View in Google Scholar
[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.
View in Google Scholar
[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.
View in Google Scholar
[14] I. Küçük, “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.
View in Google Scholar
[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.
View in Google Scholar
[16] A. Nouicer, E. Nouicer, and F. Mouloud, “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.
View in Google Scholar
[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.
View in Google Scholar
[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.
View in Google Scholar
[19] A. Krok, “Analiza wybranych zagadnień mechaniki konstrukcji i materiałów za pomocą SSN i filtrów Kalmana (Analysis of mechanics of structures and material problems applying artificial neural networks learnt by means of Kalman filtering)”, Ph.D. thesis, Tadeusz Kościuszko Cracow University of Technology, 2007 (in Polish).
View in Google Scholar
[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.
View in Google Scholar
[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.
View in Google Scholar
[22] D. Pham and D. Karaboga, Intelligent Optimisation Techniques: Genetic Algorithms, Tabu Search, Simulated Annealing and Neural Networks. Springer, 2011.
View in Google Scholar
[23] C. Bishop, Pattern Recognition and Machine Learning. Springer, 2006.
View in Google Scholar
[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.
View in Google Scholar
[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.
View in Google Scholar
[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.
View in Google Scholar
[27] R. Neal, Software for Flexible Bayesian Modeling and Markov Chain Sampling [Online]. Available: http://www.cs.toronto.edu/~radford/fbm.software.html
View in Google Scholar
[28] Optimization Toolbox User’s Guide – MathWorks, The MathWorks Inc. [Online]. Available: http://uk.mathworks.com/help/pdf-doc/optim/optim_tb.pdf
View in Google Scholar
[29] S. N. Sivanandam and S. N. Deepa, Introduction to Genetic Algorithms. Springer, 2008.
View in Google Scholar
[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.
View in Google Scholar
[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.
View in Google Scholar
Downloads
Submitted
Published
Issue
Section
License
Copyright (c) 2015 Journal of Telecommunications and Information Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
