References
- Avram, M.V., Mishra, S., Parulian, N.N. and Diesner, J. (2019). Adversarial perturbations to manipulate the perception of power and influence in networks, 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Vancouver, Canada, pp. 986–994.
- Baayen, C. and Hougaard, P. (2015). Confidence bounds for nonlinear dose–response relationships, Statistics in Medicine 34(27): 3546–3562.
- Bucur, D. and Holme, P. (2020). Beyond ranking nodes: Predicting epidemic outbreak sizes by network centralities, PLOS Computational Biology 16(7): 1–20.
- Ceccato, M., Formaggio, F. and Tomasin, S. (2020). Spatial GNSS spoofing against drone swarms with multiple antennas and Wiener filter, IEEE Transactions on Signal Processing 68(10): 5782–5794.
- Chen, H.F. and Zhao, W. (2014). Recursive Identification and Parameter Estimation, CRC Press, Boca Raton, USA.
- Diaz Ruiz, C. and Nilsson, T. (2023). Disinformation and echo chambers: How disinformation circulates on social media through identity-driven controversies, Journal of Public Policy & Marketing 42(1): 18–35.
- Frąszczak, D. (2023). Detecting rumor outbreaks in online social networks, Social Network Analysis and Mining 13(1): 91.
- Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A. and Bengio, Y. (2020). Generative adversarial networks, Communications of the ACM 63(11): 139–144.
- Hoerl, A.E. and Kennard, R.W. (1970). Ridge regression: Biased estimation for nonorthogonal problems, Technometrics 12(1): 55–67.
- Janczak, A. and Korbicz, J. (2019). Two-stage instrumental variables identification of polynomial Wiener systems with invertible nonlinearities, International Journal of Applied Mathematics and Computer Science 29(3): 571–580, DOI: 10.2478/amcs-2019-0042.
- Khoei, T.T., Ismail, S. and Kaabouch, N. (2022). Dynamic selection techniques for detecting GPS spoofing attacks on uavs, Sensors 22(2): 1–18.
- Lemieszewski, Ł., Radomska-Zalas, A., Perec, A., Dobryakova, L. and Ochin, E. (2021). The spoofing detection of dynamic underwater positioning systems (DUPS) based on vehicles retrofitted with aacoustic speakers, Electronics 10(17): 1–11.
- Li, Y., Cheng, M., Hsieh, C.J. and Lee, T.C. (2022). A review of adversarial attack and defense for classification methods, The American Statistician 76(4): 329–345.
- Mzyk, G. (2013). Nonparametric instrumental variables for identification of block-oriented systems, International Journal of Applied Mathematics and Computer Science 23(3): 521–537, DOI: 10.2478/amcs-2013-0040.
- Mzyk, G. (2014). Combined Parametric-Nonparametric Identification of Block-Oriented Systems, Lecture Notes in Control and Information Sciences, Vol. 454, Springer, Berlin.
- Norquay, S.J., Palazoglu, A. and Romagnoli, J. (1998). Model predictive control based on Wiener models, Chemical Engineering Science 53(1): 75–84.
- Reily, B., Coniff, C., Rogers, J.G. and Reardon, C. (2022). Disruption of connectivity graphs in uncertain multi-agent systems, IEEE International Conference on Omni-layer Intelligent Systems (COINS), Barcelona, Spain, pp. 1–6.
- Ribeiro, A.H. and Schön, T.B. (2023). Overparameterized linear regression under adversarial attacks, IEEE Transactions on Signal Processing 71(2): 601–614.
- Ribeiro, A.H., Zachariah, D. and Schön, T. (2022). Surprises in adversarially-trained linear regression, arXiv 2205.12695.
- Ribeiro, A.L.P., Zachariah, D., Bach, F. and Schön, T. (2023). Regularization properties of adversarially-trained linear regression, arXiv 2310.10807.
- Siyu, W. and Jian-Qiao, S. (2012). A physics-based linear parametric model of room temperature in office buildings, Building and Environment 50: 1–9.
- Söderström, T. (2018). Errors-in-Variables Methods in System Identification, Springer, Cham.
- Sunusi, A.B., Abdulkarim, I.H., Auwal, A.B. and Abhiwat, K. (2022). Optimizing Hammerstein–Wiener model for forecasting confirmed cases of COVID-19, International Journal of Applied Mathematics 52(1): 1–10.
- Tibshirani, R. (1996). Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society: Series B (Methodological) 58(1): 267–288.
- Wing, O. (2009). Circuit dynamics, Classical Circuit Theory, Springer, Boston, pp. 1–24.
- Xu, H., Caramanis, C. and Mannor, S. (2008). Robust Regression and Lasso, Advances in Neural Information Processing Systems, Vol. 21, Curran Associates, Inc., Vancouver.
- Zhang, Y., Chen, Z., Zhang, X., Sun, Q. and Sun, M. (2018). A novel control scheme for quadrotor UAV based upon active disturbance rejection control, Aerospace Science and Technology 79: 601–609.