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Kalman filter and ridge regression backpropagation algorithms | ||
| International Journal of Nonlinear Analysis and Applications | ||
| دوره 12، شماره 2، بهمن 2021، صفحه 485-493 اصل مقاله (1.59 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2021.5075 | ||
| نویسندگان | ||
| Irtefaa A. Neamah* 1؛ Zainab Abdul Redhaa2 | ||
| 1Faculty of Computer Science and Mathematics, University of Kufa, Iraq | ||
| 2Ministry of Education, Najaf, Iraq | ||
| تاریخ دریافت: 14 دی 1399، تاریخ بازنگری: 08 اسفند 1399، تاریخ پذیرش: 18 اسفند 1399 | ||
| چکیده | ||
| The Kalman filter (KF) compare with the ridge regression backpropagation algorithm (RRBp) by conducting a numerical simulation study that relied on generating random data applicable to the KF and the RRBp in different sample sizes to determine the performance and behavior of the two methods. After implementing the simulation, the mean square error (MSE) value was calculated, which is considered a performance measure, to find out which two methods are better in making an estimation for random data. After obtaining the results, we find that the Kalman filter has better performance, the higher the randomness and noise in generating the data, while the other algorithm is suitable for small sample sizes and where the noise ratios are lower. | ||
| کلیدواژهها | ||
| Kalman filter؛ ridge regression؛ backpropagation algorithms؛ estimation | ||
| مراجع | ||
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[1] N. Bisoyi, H. Gupta, N. Padhy and G. Chakrapani, Prediction of daily sediment discharge using a back propagation neural network training algorithm: A case study of the Narmada River, India, Int. J. Sediment Res. 2018. [2] L. Das, N. Kumar, R. Lather and P. Bhatia, Emerging Trends in Mechanical Engineering: Select Proceedings of ICETMIE 2019, Springer, 2021. [3] Y. Deng, H. Zeng, Y. Jiang, G. Chen, J. Chen and L. Sun, Ridge regression for predicting elastic moduli and hardness of calcium aluminosilicate glasses, Materials Research Express, 2018. [4] F. Govaers, Introduction and Implementations of the Kalman Filter, InTechOpen, 2018. [5] A. Hameed, B. Karlik and M. S. Salman, ”Back-propagation Algorithm with Variable Adaptive Momentum, Knowledge-Based Systems, 2016. [6] S. Haykin, Kalman Filtering and Neural Networks, Wiley-Interscience, 2001. [7] Y. Huang, Y. Zhang, Z. Wu, N. Li and J. Chambers, A Novel Robust Student’s t-Based Kalman Filter, IEEE Transactions on Aerospace and Electronic Systems, 2017. [8] N. Jeremia, S. Nurrohmah and I. Fithriani, Robust Ridge regression to solve a multicollinearity and outlier, J. Physics: Conference Series, 2020. [9] D. Kim and C. Park, Application of Kalman Filter for Estimating a Process Disturbance in a Building Space, Sustainability, 2017. [10] R. Kleinbauer, Kalman Filtering Implementation with Matlab, Universit¨at Stuttgart, 2004. [11] T. Lillicrap, A. Santoro, L. Marris, C. Akerman and G. Hinton, Backpropagation and the brain, Nature Reviews Neuroscience, 2020. [12] X. Liu, S. Zheng and X. Feng, Estimation of error variance via ridge regression, Biometrika, 2020. [13] A. Mansour, G. Salh and H. Zeen Alabdeen, Voice recognition Using back propagation algorithm in neural networks, Int. J. Comput. Trends Tech. 2015. [14] H. Ma, L. Yan, Y. Xia and M. Fu, Kalman Filtering and Information Fusion, Springer, 2020. [15] N. Ma, S. Zhao, Z. Sun, X. Wu and Y. Zhai, An improved ridge regression algorithm and its application in predicting TV ratings, Multimedia Tools Appl. 2017. [16] G. McDonald, Ridge Regression, Wiley Interdisciplinary Reviews Computational Statistics (WIREs), 2009. [17] A. Moghaddam, M. Moghaddam and M. Esfandyari, Stock market index prediction using artificial neural network, J. Econ. Fin. Adm. Sci. 2016. [18] A. Muayad and A. Irtefaa, Ridge Regression using Artificial Neural Network, Indian J. Sci. Tech. 2016. [19] N. Nawi, N. Zaidi, N. Hamid, M. Rehman, A. Ramli and S. Kasim, Optimal parameter selection using three-term back propagation algorithm for data classification, International Journal on Advanced Science, Engineering and Information Technology, 2017. [20] K. Singh, S. Kumar, P. Dixit and M. Bajpai, Kalman Filter Based Short Term Prediction Model for COVID-19 Spread, Applied Intelligence, 2020. [21] S. Setti and A. Wanto, Analysis of backpropagation algorithm in predicting the most number of internet users in the world, J. Online Info. (JOIN), 2018. [22] J. Su, Y. Chen, D. Zhang and Y. Kang, Full Parameter Identification Model based on Back Propagation Algorithm for Brushless Doubly Fed Induction Generator, IEEE Transactions on Power Electronics, 2020. [23] G. Welch and G. Bishop, An Introduction to the Kalman Filter, University of North Carolina at Chapel Hill , TR, 2006. [24] J. Xie and S. Dubljevic, Discrete-Time Kalman Filter Design for Linear Infinite-Dimensional Systems, Processes,2019. [25] Y. Xu and X. Wu, An affine subspace clustering algorithm based on ridge regression, Pattern Analysis and Applications, 2016. [26] X. Zhang, X. Chen and J. Li, Improving Dam Seepage Prediction Using Back-Propagation Neural Network and Genetic Algorithm, Mathematical Problems in Engineering, 2020. [27] S. Zhao and B. Huang, Trial-and-error or avoiding a guess? Initialization of the Kalman filter, Automatica, 2020. [28] K. R. A new approach to linear filtering and prediction problems, ASME. J. Basic Eng. 82(1) (1960) 35—45. | ||
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