Identification of nonlinear system with time delay based on wavelet packet decomposition and Gaussian kernel GMDH network

References

• Askari, S. (2021). Fuzzy C-means clustering algorithm for data with unequal cluster sizes and contaminated with noise and outliers: Review and development. Expert Systems with Applications, 165, Article 113856. https://doi.org/10.1016/j.eswa.2020.113856
   Web of Science ®Google Scholar
• Chang, W. D. (2022). Identification of nonlinear discrete systems using a new Hammerstein model with Volterra neural network. Soft Computing, 26(14), 6765–6775. https://doi.org/10.1007/s00500-022-07089-6
   Web of Science ®Google Scholar
• Chaudhary, N. I., Aslam, M. S., & Baleanu, D. (2020). Design of sign fractional optimization paradigms for parameter estimation of nonlinear Hammerstein systems. Neural Computing and Applications, 32(12), 8381–8399. https://doi.org/10.1007/s00521-019-04328-0
   Web of Science ®Google Scholar
• Chen, H. L., Zhang, Q., Luo, J., Xu, Y. T., & Zhang, X. Q. (2020). An enhanced bacterial foraging optimization and its application for training kernel extreme learning machine. Applied Soft Computing, 86, Article 105884. https://doi.org/10.1016/j.asoc.2019.105884
   Web of Science ®Google Scholar
• Chen, T., Zeng, C., & Wang, C. (2022). Fault identification for a class of nonlinear systems of canonical form via deterministic learning. IEEE Transactions on Cybernetics, 52(10), 10957–10968. https://doi.org/10.1109/TCYB.2021.3072645
   PubMed Web of Science ®Google Scholar
• Cui, T., Ding, F., Alsaedi, A., & Hayat, T. (2020). Data filtering-based parameter and state estimation algorithms for state-space systems disturbed by coloured noises. International Journal of Systems Science, 51(9), 1669–1684. https://doi.org/10.1080/00207721.2020.1772403
   Web of Science ®Google Scholar
• Ding, F., Liu, X. M., & Hayat, T. (2020). Hierarchical least squares identification for feedback nonlinear equation-error systems. Journal of the Franklin Institute, 357(5), 2958–2977. https://doi.org/10.1016/j.jfranklin.2019.12.007
   Web of Science ®Google Scholar
• Elbaz, K., Shen, S. L., Zhou, A., Yin, Z. Y., & Lyu, H. M. (2021). Prediction of disc cutter life during shield tunneling with AI via the incorporation of a genetic algorithm into a GMDH-type neural network. Engineering, 7(2), 238–251. https://doi.org/10.1016/j.eng.2020.02.016
   Web of Science ®Google Scholar
• Gu, X., Shen, Q., & Angelov, P. P. (2021). Particle swarm optimized autonomous learning fuzzy system. IEEE Transactions on Cybernetics, 51(11), 5352–5363. https://doi.org/10.1109/TCYB.2020.2967462
   PubMed Web of Science ®Google Scholar
• Hewing, L., Kabzan, J., & Zeilinger, M. N. (2020). Cautious model predictive control using Gaussian process regression. IEEE Transactions on Control Systems Technology, 28(6), 2736–2743. https://doi.org/10.1109/TCST.87
   Web of Science ®Google Scholar
• Huang, Y. M., Dai, X. Y., Wang, Q. W., & Zhou, D. Q. (2021). A hybrid model for carbon price forecastingusing GARCH and long short-term memory network. Applied Energy, 285, Article 116485. https://doi.org/10.1016/j.apenergy.2021.116485
   Web of Science ®Google Scholar
• Kajal, K., Utkal, M., Vineet, P., & Jito, V. (2020). Identification scheme for fractional Hammerstein models with the delayed haar wavelet. IEEE/CAA Journal of Automatica Sinica, 7(3), 882–891. https://doi.org/10.1109/JAS.6570654
   Google Scholar
• Kou, J., Zhang, W., & Yin, M. (2016). Novel Wiener models with a time-delayed nonlinear block and their identification. Nonlinear Dynamics, 85(4), 2389–2404. https://doi.org/10.1007/s11071-016-2833-y
   Web of Science ®Google Scholar
• Li, J. H., Zong, T. C., & Lu, G. P. (2022). Parameter identification of Hammerstein–Wiener nonlinear systems with unknown time delay based on the linear variable weight particle swarm optimization. ISA Transactions, 120, 89–98. https://doi.org/10.1016/j.isatra.2021.03.021
   PubMed Web of Science ®Google Scholar
• Li, Y. R., Chen, W. X., Chen, J., Chen, X., Liang, J., & Du, M. (2019). Neural network based modeling and control of elbow joint motion under functional electrical stimulation. Neurocomputing, 340, 171–179. https://doi.org/10.1016/j.neucom.2019.03.003
   Web of Science ®Google Scholar
• Liu, K., Li, Y., Hu, X., Lucu, M., & Widanage, W. D. (2020). Gaussian process regression with automatic relevance determination kernel for calendar aging prediction of lithium-ion batteries. IEEE Transactions on Industrial Informatics, 16(6), 3767–3777. https://doi.org/10.1109/TII.9424
   Web of Science ®Google Scholar
• Liu, X., Wang, C., & Han, G. (2022). LPV time-delay system identification and its application to the centralized heat-supply system. IEEE Transactions on Instrumentation and Measurement, 71, 1–11.
   Web of Science ®Google Scholar
• Lu, C., Feng, J., Chen, Y., Liu, W., Lin, Z., & Yan, S. (2020). Tensor robust principal component analysis with a new tensor nuclear norm. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(4), 925–938. https://doi.org/10.1109/TPAMI.34
   PubMed Web of Science ®Google Scholar
• Ma, J. X., Ding, F., Xiong, W. L., & Yang, E. F. (2017). Combined state and parameter estimation for Hammerstein systems with time delay using the kalman filtering. International Journal of Adaptive Control and Signal Processing, 31(8), 1139–1151. https://doi.org/10.1002/acs.v31.8
   Web of Science ®Google Scholar
• Malik, M. F., Chang, C. L., & Aslam, M. S. (2022). Fuzzy-evolution computing paradigm for fractional Hammerstein control autoregressive systems. International Journal of Fuzzy Systems, 24(5), 2447–2475. https://doi.org/10.1007/s40815-022-01291-2
   Web of Science ®Google Scholar
• Mehmood, A., Zameer, A., & Chaudhary, N. I. (2020). Design of meta-heuristic computing paradigms for Hammerstein identification systems in electrically stimulated muscle models. Neural Computing and Applications, 32(16), 12469–12497. https://doi.org/10.1007/s00521-020-04701-4
   Web of Science ®Google Scholar
• Piao, H., Cheng, D., Chen, C., Wang, Y., Wang, P., & Pan, X. (2022). A high-accuracy CO 2 carbon isotope sensing system using subspace identification of Hammerstein model for geochemical application. IEEE Transactions on Instrumentation and Measurement, 71, 1–9.
   Web of Science ®Google Scholar
• Quaranta, G., Lacarbonara, W., & Masri, S. F. (2020). A review on computational intelligence for identification of nonlinear dynamical systems. Nonlinear Dynamics, 99(2), 1709–1761. https://doi.org/10.1007/s11071-019-05430-7
   Web of Science ®Google Scholar
• Rahmani, M. R., & Farrokhi, M. (2020). Fractional-order Hammerstein state-space modeling of nonlinear dynamic systems from input–output measurements. ISA Transactions, 96, 177–184. https://doi.org/10.1016/j.isatra.2019.06.015
   PubMed Web of Science ®Google Scholar
• Roshani, M., Phan, G., Roshani, G. H., Hanus, R., Nazemi, B., Corniani, E., & Nazemi, E. (2021). Combination of X-ray tube and GMDH neural network as a nondestructive and potential technique for measuring characteristics of gas-oil–water three phase flows. Measurement, 168, Article 108427. https://doi.org/10.1016/j.measurement.2020.108427
   Web of Science ®Google Scholar
• Ruffa, F., De Capua, C., Morello, R., & Liu, Z. (2019). Temperature sensing and evaluation of thermal effects on battery packs for automotive applications. IEEE Sensors Journal, 19(23), 11634–11645. https://doi.org/10.1109/JSEN.7361
   Web of Science ®Google Scholar
• Shaikh, M. A. H., & Barbé, K. (2019). Wiener–Hammerstein system identification: A fast approach through spearman correlation. IEEE Transactions on Instrumentation and Measurement, 68(5), 1628–1636. https://doi.org/10.1109/TIM.19
   Web of Science ®Google Scholar
• Shaikh, M. A. H., & Barbé, K. (2021). Study of random forest to identify Wiener–Hammerstein system. IEEE Transactions on Instrumentation and Measurement, 70, 1–12. https://doi.org/10.1109/TIM.19
   PubMed Web of Science ®Google Scholar
• Sheikholeslami, M., Barzegar Gerdroodbary, M., Moradi, R., Shafee, A., & Li, Z. X. (2019). Application of neural network for estimation of heat transfer treatment of Al 2O 3-H 2O nanofluid through a channel. Computer Methods in Applied Mechanics and Engineering, 344, 1–12. https://doi.org/10.1016/j.cma.2018.09.025
   Web of Science ®Google Scholar
• Shen, J., Zhang, X., Wang, G., Ding, Z., & Hu, B. (2022). An improved empirical mode decomposition of electroencephalogram signals for depression detection. IEEE Transactions on Affective Computing, 13(1), 262–271. https://doi.org/10.1109/TAFFC.2019.2934412
   Web of Science ®Google Scholar
• Tran, M. Q., Liu, M. K., & Elsisi, M. (2022). Effective multi-sensor data fusion for chatter detection in milling process. ISA Transactions, 125, 514–527. https://doi.org/10.1016/j.isatra.2021.07.005
   PubMed Web of Science ®Google Scholar
• Vineet, P., & Utkal, M. (2021). Modeling and parametric identification of Hammerstein systems with time delay and asymmetric dead-zones using fractional differential equations. Mechanical Systems and Signal Processing, 167, Article 108568.
   Web of Science ®Google Scholar
• Wang, H. Y., Luo, C., & Wang, X. Y. (2019). Synchronization and identification of nonlinear systems by using a novel self-evolving interval type-2 fuzzy LSTM-neural network. Engineering Applications of Artificial Intelligence, 81, 79–93. https://doi.org/10.1016/j.engappai.2019.02.002
   Web of Science ®Google Scholar
• Xu, H., Alsaedi, A., & Hayat, T. (2021). Parameter estimation for an exponential autoregressive time series model by the Newton search and multi-innovation theory. International Journal of Systems Science, 52(12), 2630–2645. https://doi.org/10.1080/00207721.2021.1895356
   Web of Science ®Google Scholar
• Xu, K. K., Yang, H. D., & Zhu, C. J. (2019). A novel extreme learning machine-based Hammerstein-Wiener model for complex nonlinear industrial processes. Neurocomputing, 358, 246–254. https://doi.org/10.1016/j.neucom.2019.05.049
   Web of Science ®Google Scholar
• Xu, L., Ding, F., & Zhu, Q. M. (2021). Decomposition strategy-based hierarchical least mean square algorithm for control systems from the impulse responses. International Journal of Systems Science, 52(9), 1806–1821. https://doi.org/10.1080/00207721.2020.1871107
   Web of Science ®Google Scholar
• Zhang, M., Wang, X., Yang, D., & Christensen, M. G. (2021). Artificial neural network based identification of multi-operating-point impedance model. IEEE Transactions on Power Electronics, 36(2), 1231–1235. https://doi.org/10.1109/TPEL.63
   Web of Science ®Google Scholar
• Zhang, Q. (2023). Hybrid identification method for fractional-order nonlinear systems based on the multi-innovation principle. Applied Intelligence, 53(12), 15711–15726. https://doi.org/10.1007/s10489-022-04309-2
   Web of Science ®Google Scholar
• Zhang, X., & Ding, F. (2020). Hierarchical parameter and state estimation for bilinear systems. International Journal of Systems Science, 51(2), 275–290. https://doi.org/10.1080/00207721.2019.1704093
   Web of Science ®Google Scholar
• Zong, T. C., Li, J. H., & Lu, G. P. (2023). Bias-compensated least squares and fuzzy PSO based hierarchical identification of errors-in-variables Wiener systems. International Journal of Systems Science, 54(3), 633–651. https://doi.org/10.1080/00207721.2022.2135976
   Web of Science ®Google Scholar