Ljung, L.: Perspectives on system identification. Ann. Rev. Control 34, 1–12 (2010)
Sjöberg, J., Zhang, Q., Delyon, B., Glorennec, P.-Y., Hjalmarsson, H., Ljung, L., Benveniste, A., Juditsky, A.: Nonlinear black-box modeling in system identification: a unified overview. Automatica 31, 1691–1724 (1995)
Fu, L., Li, P.: The research survey of system identification method. In: 5th IEEE International Conference on Intelligent Human–Machine Systems and Cybernetics, pp. 397–401 (2013)
Kumar, N., Jha, G.K.: A time series ANN approach for weather forecasting. Int. J. Control Theory Comput. Model 3, 19–25 (2013)
Liu, G.: Nonlinear Identification and Control: A Neural Network Approach. Springer, London (2001)
Wang, C., Hill, D.J.: Learning from neural control. IEEE Trans. Neural Netw. 17, 130–146 (2006)
Yazdizadeh, A., Khorasani, K., Patel, R.V.: Identification of a two-link flexible manipulator using adaptive time delay neural networks. IEEE Trans. Syst. Man Cybern. B 30, 165–172 (2000)
He, W., Li, Y., Ge, W., Liu, Y.-J.: Model identification and control design for a humanoid robot. IEEE Trans. Syst. Man Cybern. 47, 45–57 (2017)
Yang, C., Huang, K., Li, Y., Su, C.-Y.: Haptic identification by ELM-controlled uncertain manipulator. IEEE Trans. Syst. Man Cybern. 99, 1–12 (2017)
Lu, J., Huang, J., Lu, F.: Time series prediction based on adaptive weight online sequential extreme learning machine. Appl. Sci. 217, 1–14 (2017)
Huang, F., Huang, J., Jiang, S., Zhou, C.: Ladslide displacement prediction based on multivariate chaotic model and extreme learning machine. Eng. Geol. 218, 173–186 (2017)
Henríquez, P.A., Ruz, G.A.: Extreme learning machine with a deterministic assignment of hidden weights in two parallel layers. Neurocomputing 226, 109–116 (2017)
Wang, S., Wang, W., Tang, Y., Liu, F., Guan, X.: Identification of chaotic system using Hammerstein-ELM model. Nonlinear Dyn. 81, 1081–1095 (2015)
Gastaldo, P., Bisio, F., Gianoglio, C., Ragusa, E., Zunino, R.: Learning with similarity functions: a novel design for the extreme learning machine. Neurocomputing 261, 37–49 (2017)
Huang, G., Zhu, Q.-Y., Siew, C.-K.: Extreme learning machine: theory and applications. Neurocomputing 70, 489–501 (2006)
Huang, G., Ding, X., Zhou, H., Zhang, R.: Extreme learning machine for regression and multiclass classification. IEEE Trans. Syst. Man Cybern. 42, 513–529 (2012)
Huang, G., Wang, D.H., Luan, Y.: Extreme learning machines: a survey. Int. J. Mach. Learn. Cybern. 2, 107–122 (2011)
Zhao, G., Shen, Z., Miao, C., Man, Z.: On improving the conditioning of extreme learning machine: a linear case. In: 7th International Conference on Information Communications and Signal Processing, pp. 1–5 (2009)
Janakiraman, V.M., Assanis, D.: Lyapunov Method Based Online Identification of Nonlinear Systems Using Extreme Learning Machines. Computing Research Repository (CoRR), pp. 1–8. arXiv:1211.1441 (2012)
Duan, J., Ou, Y., Hu, J., Wang, Z., Jin, S., Xu, C.: Fast and stable learning of dynamical systems based on extreme learning machine. IEEE Trans. Syst. Man Cybern. 99, 1–11 (2017)
Ioannou, P.A., Sun, J.: Robust Adaptive Control. Dover Publications, New York (2012)
Vargas, J.A.R., Hemerly, E.M., Villarreal, E.R.L.: Stability analysis of a neuro-identification scheme with asymptotic convergence. Int. J. Artif. Intell. Appl. 3, 35–50 (2012)
Grzeidak, E., Vargas, J.A.R., Alfaro, S.C.A.: Online neuro-identification of nonlinear systems using extreme learning machine. In: International Joint Conference on Neural Networks (IJCNN), pp. 2923–2930 (2016)
Yu, W., Poznyak, A.S., Li, X.: Multilayer dynamic neural networks for non-linear system on-line identification. Int. J. Control 74, 1858–1864 (2001)
Huang, G., Babri, H.A.: Upper bounds on the number of hidden neurons in feedforward networks with arbitrary bounded nonlinear. IEEE Trans. Neural Netw. 17, 879–892 (1998)
Wang, Y., Cao, F., Yuan, Y.: A study on effectiveness of extreme learning machine. Neurocomputing 74, 2483–2490 (2011)
Ding, S., Zhao, H., Zhang, Y., Xu, X., Nie, R.: Extreme learning machine: algorithm, theory and applications. Artif. Intell. Rev. 44, 103–115 (2015)
Ge, S.S., Hang, C.C., Lee, T.H., Zhang, T.: Stable Adaptive Neural Network Control. Kluwer, Boston (2001)
Hornik, K., Stinchcombe, M., White, H.: Multilayer feedforward networks are universal approximators. Neural Netw. 2, 359–366 (1989)
Funahashi, K.-I.: On the approximate realization of continuous mappings by neural networks. IEEE Int. Conf. Neural Netw. 1, 183–192 (1989)
Paretto, P., Niez, J.: Long term memory storage capacity of multiconnected neural networks. Biol. Cybern. 54, 53–63 (1986)
Giles, C., Maxwell, T.: Learning, invariance, and generalization in higher order neural networks. Appl. Opt. 26, 4972–4978 (1987)
Kosmatopoulos, E.B., Polycarpou, M.M., Christodoulou, M.A.: Higher-order neural network structures for identification of dynamical systems. IEEE Trans. Neural Netw. 6, 422–431 (1995)
Vargas, J.A.R., Hemerly, E.M.: Neural adaptive observer with asymptotic convergence in the presence of time-varying parameters and disturbances. Sba Controle Autom. (in Portuguese) 19, 18–29 (2008)
Slotine, J.J.E., Li, W.: Applied Nonlinear Control. Prentice-Hall International, Englewood Cliffs (1991)
Khalil, H.K.: Nonlinear Systems. Prentice-Hall International, Englewood Cliffs (2002)
De la Rosa, E., Yu, W., Li, X.: Nonlinear system modeling with deep neural networks and autoencoders algorithm. In: IEEE International Conference on Systems, Man, and Cybernetics, pp. 1–8 (2016)
Nelles, O.: Nonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models. Springer, Berlin (2013)
L, J., Chen, D., Celikovsky, S.: Bridge the gap between the Lorenz system and Chen system. Int. J. Bifurc. Chaos 12, 2917–2926 (2002)
Yu, H., Cai, G., Li, Y.: Dynamic analysis and control of a new hyperchaotic finance system. Nonlinear Dyn. 67, 2171–2182 (2012)
Mahmoud, E.E.: Dynamics and synchronization of new hyperchaotic complex Lorenz system. Math. Comput. Model. 55, 1951–1962 (2012)