[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.