A journal of IEEE and CAA , publishes
high-quality papers in English on original
theoretical/experimental research
and development in all areas of automation
Volume 9
Issue 9
IEEE/CAA Journal of Automatica Sinica
| Citation: | F. Li, T. Zheng, N. B. He, and Q. F. Cao, “Data-driven hybrid neural fuzzy network and ARX modeling approach to practical industrial process identification,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 9, pp. 1702–1705, Sept. 2022. doi: 10.1109/JAS.2022.105821
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K. Kothari, U. Mehta, V. Prasad, and J. Vanualailai, “Identification scheme for fractional Hammerstein models with the delayed Haar wavelet,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 3, pp. 882–891, May 2020. doi: 10.1109/JAS.2020.1003093
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K. Hammar, T. Djamah, and M. Bettayeb, “Identification of fractional Hammerstein system with application to a heating process,” Nonlinear Dynamics, vol. 96, pp. 2613−2626, Apr. 2019.
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D. Wang, S. Zhang, M. Gan, and J. Qiu, “A novel EM identification method for Hammerstein systems with missing output data,” IEEE Trans. Industrial Informatics, vol. 16, no. 4, pp. 2500−2508, Apr. 2020.
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S. Dong, L. Yu, W. A. Zhang, and B. Chen, “Robust extended recursive least squares identification algorithm for Hammerstein systems with dynamic disturbances,” Digital Signal Processing, vol. 101, p. 102716, Mar. 2020.
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Y. Ji, Z. Kang, and X. Liu, “The data filtering based multiple-stage Levenberg-Marquardt algorithm for Hammerstein nonlinear systems,” Int. J. Robust and Nonlinear Control, vol. 31, no. 15, pp. 7007−7025, Jun. 2021.
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Y. Ji, X. Jiang, and L. Wan, “Hierarchical least squares parameter estimation algorithm for two-input Hammerstein finite impulse response systems,” J. Franklin Institute, vol. 357, pp. 5019−5032, Mar. 2020.
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Z. Q. Lang, “On identification of the controlled plants described by the Hammerstein systems,” IEEE Trans. Automat Control, vol. 39, no. 3, pp. 569−573, Jan. 1994.
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S. Li, Y. Ge, and Y. Shi, “An iterative dynamic programming optimization based on biorthogonal spatial-temporal Hammerstein modeling for the enhanced oil recovery of ASP flooding,” J. Process, vol. 73, pp. 75−88, Jan. 2019.
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F. Li and L. Jia, “Correlation analysis-based error compensation recursive least-square identification method for the Hammerstein model,” J. Statistical Computation and Simulation, vol. 88, no. 1, pp. 56−74, Sept. 2017.
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L. Vanbeylen, R. Pintelon, and J. Schoukens, “Blind maximum likelihood identification of Hammerstein systems,” Automatica, vol. 44, no. 12, pp. 3139−3146, Nov. 2008.
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M. Schoukens, R. Pintelon, and Y. Rolain, “Parametric identification of parallel Hammerstein systems,” IEEE Trans. Instrumentation and Measurement, vol. 60, no. 12, pp. 3931−3938, Apr. 2011.
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L. Abualigah, D. Yousri, M. Abd Elaziz, A. A. Ewees, M. A. A. Al-qaness, and A. H. Gandomi, “Aquila optimizer: A novel meta-heuristic optimization algorithm,” Computers & Industrial Engineering, vol. 157, p. 107250, Mar. 2021.
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J. O. Agushakaa, A. E. Ezugwua, and L. Abualigah, “Dwarf mongoose optimization algorithm,” Computer Methods in Applied Mechanics and Engineering, vol. 391, p. 114570, Jan. 2022.
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O. N. Oyelade, A. E. Ezugwu, T. Mohamed and L. Abualigah, “Ebola optimization search algorithm: A new nature-inspired metaheuristic optimization algorithm,” IEEE Access, vol. 10, 16150−16177, Jan. 2022.
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L. Abualigah, M. Abd Elaziz, P. Sumari, G. Zong Woo, and A. H. Gandomi, “Reptile search algorithm (RSA): A novel nature-inspired meta-heuristic optimizer,” Expert Systems with Applications, vol. 191, p. 116158, Apr. 2022.
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X. Zhu, F. Li, C. Li, L. Jia, and Q. Cao, “Parameter estimation of the Hammerstein output error model using multi-signal processing,” in Proc. 10th IEEE Data Driven Control and Learning Syst. Conf., pp. 1285−1290, Jun. 2021.
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F. Li, K. Yao, B. Li, and L. Jia, “A novel learning algorithm of the neuro-fuzzy based Hammerstein-Wiener model corrupted by process noise,” J. Franklin Institute, vol. 38, no. 3, pp. 2115−2137, Jan. 2021.
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F. Li, L. Jia, and D. Peng, “Identification method of neuro-fuzzy-based Hammerstein model with coloured noise,” IET Control Theory and Applications, vol. 11, no. 17, pp. 3026−3037, Sept. 2017.
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W. Ipanaqué and J. Manrique, “Identification and control of pH using optimal piecewise linear wiener model,” IFAC Proceedings Volumes, vol. 44, no. 1, pp. 12301−12306, Jan. 2011.
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F. Ding, H. Chen, L. Xu, J. Dai, Q. Li, and T. Hayat, “A hierarchical least squares identification algorithm for Hammerstein nonlinear systems using the key term separation,” J. Franklin Institute, vol. 355, no. 8, pp. 3737−3752, Dec. 2018.
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Z. Wang, Y. Shen, Z. Ji, and R. Ding, “Filtering based recursive least squares algorithm for Hammerstein FIR-MA systems,” Nonlinear Dynamics, vol. 73, pp. 1045−1054, Mar. 2013.
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X. Yuan, S. Qi, Y. Wang, and H. Xia, “A dynamic CNN for nonlinear dynamic feature learning in soft sensor modeling of industrial process data,” Control Engineering Practice, vol. 104, p. 104614, Nov. 2020.
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