Maximum likelihood-based recursive least-squares estimation for multivariable systems using the data filtering technique

References

• Cao, Y., Li, P., & Zhang, Y. (2018). Parallel processing algorithm for railway signal fault diagnosis data based on cloud computing. Future Generation Computer Systems, 88, 279–283.
   Web of Science ®Google Scholar
• Cao, Y., Lu, H., & Wen, T. (2019). A safety computer system based on multi-sensor data processing. Sensors, 19(4), 818. doi:10.3390/s19040818
   Web of Science ®Google Scholar
• Cao, Y., Ma, L. C., Xiao, S., Zhang, X., & Xu, W. (2017). Standard analysis for transfer delay in CTCS-3. Chinese Journal of Electronics, 26(5), 1057–1063.
   Web of Science ®Google Scholar
• Cao, Y., Wen, Y., Meng, X., & Xu, W. (2016). Performance evaluation with improved receiver design for asynchronous coordinated multipoint transmissions. Chinese Journal of Electronics, 25(2), 372–378.
   Web of Science ®Google Scholar
• Cao, Y., Zhang, Y., Wen, T., & Li, P. (2019). Research on dynamic nonlinear input prediction of fault diagnosis based on fractional differential operator equation in high-speed train control system. Chaos, 29(1). Article Number: 013130. doi:10.1063/1.5085397
   Web of Science ®Google Scholar
• Chen, F. Y., Ding, F., Alsaedi, A., & Hayat, T. (2017). Data filtering based multi-innovation extended gradient method for controlled autoregressive autoregressive moving average systems using the maximum likelihood principle. Mathematics and Computers in Simulation, 132, 53–67.
   Web of Science ®Google Scholar
• Chen, G. Y., Gan, M., Chen, C. L. P., & Li, H. X. (2019). A regularized variable projection algorithm for separable nonlinear least-squares problems. IEEE Transactions on Automatic Control, 64(2), 526–537.
   Web of Science ®Google Scholar
• Chen, G. Y., Gan, M., Ding, F., & Chen, C. L. P. (2019). Modified Gram-Schmidt method-based variable projection algorithm for separable nonlinear models. IEEE Transactions on Neural Networks and Learning Systems. doi:10.1109/TNNLS.2018.2884909
   Google Scholar
• Ding, J. L. (2017). The hierarchical iterative identification algorithm for multi-input-output-error systems with autoregressive noise. Complexity, 1–11. Article ID 5292894. doi: 10.1155/2017/5292894
   Web of Science ®Google Scholar
• Ding, J. L. (2018). Recursive and iterative least squares parameter estimation algorithms for multiple-input-output-error systems with autoregressive noise. Circuits Systems and Signal Processing, 37(5), 1884–1906.
   Web of Science ®Google Scholar
• Ding, F., Chen, H. B., Xu, L., Dai, J. Y., Li, Q. S., & Hayat, T. (2018). A hierarchical least squares identification algorithm for Hammerstein nonlinear systems using the key term separation. Journal of the Franklin Institute, 355(8), 3737–3752.
   Web of Science ®Google Scholar
• Ding, F., Wang, Y. J., Dai, J. Y., Li, Q. S., & Chen, Q. J. (2017). A recursive least squares parameter estimation algorithm for output nonlinear autoregressive systems using the input-output data filtering. Journal of the Franklin Institute, 354(15), 6938–6955.
   Web of Science ®Google Scholar
• Ding, F., Xu, L., Alsaadi, F. E., & Hayat, T. (2018). Iterative parameter identification for pseudo-linear systems with ARMA noise using the filtering technique. IET Control Theory and Applications, 12(7), 892–899.
   Web of Science ®Google Scholar
• Ding, F., Xu, L., & Zhu, Q. M. (2016). Performance analysis of the generalized projection identification for time-varying systems. IET Control Theory and Applications, 10(18), 2506–2514.
   Web of Science ®Google Scholar
• Gan, M., Li, H. X., & Peng, H. (2015). A variable projection approach for efficient estimation of RBF-ARX model. IEEE Transactions on Cybernetics, 45(3), 462–471.
   Web of Science ®Google Scholar
• Gong, P. C., Wang, W. Q., Li, F. C., & Cheung, H. (2018). Sparsity-aware transmit beamspace design for FDA-MIMO radar. Signal Processing, 144, 99–103.
   Web of Science ®Google Scholar
• Gu, Y., Liu, J., Chou, Y., & Ji, Y. (2019). Moving horizon estimation for multirate systems with time-varying time-delays. Journal of the Franklin Institute, 356(4), 2325–2345.
   Web of Science ®Google Scholar
• Gu, Y., Liu, J., Li, X., Chou, Y., & Ji, Y. (2019). State space model identification of multirate processes with time-delay using the expectation maximization. Journal of the Franklin Institute, 356(3), 1623–1639.
   Web of Science ®Google Scholar
• Li, M. H., & Liu, X. M. (2018). Auxiliary model based least squares iterative algorithms for parameter estimation of bilinear systems using interval-varying measurements. IEEE Access, 6, 21518–21529.
   Web of Science ®Google Scholar
• Li, M. H., & Liu, X. M. (2018). The least squares based iterative algorithms for parameter estimation of a bilinear system with autoregressive noise using the data filtering technique. Signal Processing, 147, 23–34.
   Web of Science ®Google Scholar
• Li, J. H., Zheng, W. X., Gu, J. P., & Hua, L. (2018). A recursive identification algorithm for Wiener nonlinear systems with linear state-space subsystem. Circuits Systems and Signal Processing, 37(6), 2374–2393.
   Web of Science ®Google Scholar
• Liu, F. (2019). Boundedness and continuity of maximal operators associated to polynomial compound curves on Triebel-Lizorkin spaces. Mathematical Inequalities & Applications, 22(1), 25–44.
   Web of Science ®Google Scholar
• Liu, Q. Y., & Ding, F. (2019). Auxiliary model-based recursive generalized least squares algorithm for multivariate output-error autoregressive systems using the data filtering. Circuits Systems and Signal Processing, 38(2), 590–610.
   Web of Science ®Google Scholar
• Liu, S. Y., Ding, F., Xu, L., & Hayat, T. (2019). Hierarchical principle-based iterative parameter estimation algorithm for dual-frequency signals. Circuits Systems and Signal Processing, 38. doi:10.1007/s00034-018-1015-1
   Google Scholar
• Liu, F., Fu, Z., & Jhang, S. (2019). Boundedness and continuity of Marcinkiewicz integrals associated to homogeneous mappings on Triebel-Lizorkin spaces. Frontiers of Mathematics in China, 14(1), 95–122.
   Web of Science ®Google Scholar
• Liu, F., Xue, Q., & Yabuta, K. (2019). Boundedness and continuity of maximal singular integrals and maximal functions on Triebel-Lizorkin spaces. Science China-Mathematics. doi:10.1007/s11425-017-9416-5
   Google Scholar
• Ma, P., Ding, F., & Zhu, Q. M. (2018). Decomposition-based recursive least squares identification methods for multivariate pseudolinear systems using the multi-innovation. International Journal of Systems Science, 49(5), 920–928.
   Web of Science ®Google Scholar
• Na, J., Herrmann, G., & Zhang, K. Q. (2017). Improving transient performance of adaptive control via a modified reference model and novel adaptation. International Journal of Robust and Nonlinear Control, 27(8), 1351–1372.
   Web of Science ®Google Scholar
• Na, J., Huang, Y., Wu, X., Gao, G. B., Herrmann, G., & Jiang, J. Z. (2018). Active adaptive estimation and control for vehicle suspensions with prescribed performance. IEEE Transactions on Control Systems Technology, 26(6), 2063–2077.
   Web of Science ®Google Scholar
• Na, J., Yang, J., Wu, X., & Guo, Y. (2015). Robust adaptive parameter estimation of sinusoidal signals. Automatica, 53, 376–384.
   Web of Science ®Google Scholar
• Pan, J., Li, W., & Zhang, H. P. (2018). Control algorithms of magnetic suspension systems based on the improved double exponential reaching law of sliding mode control. International Journal of Control Automation and Systems, 16(6), 2878–2887.
   Web of Science ®Google Scholar
• Pan, J., Ma, H., Jiang, X., Ding, W. F., & Ding, F. (2018). Adaptive gradient-based iterative algorithm for multivariate controlled autoregressive moving average systems using the data filtering technique. Complexity. Article ID 9598307. doi:10.1155/2018/9598307
   PubMed Web of Science ®Google Scholar
• Pastell, M., Frondelius, L., Jarvinen, M., & Backman, J. (2018). Filtering methods to improve the accuracy of indoor positioning data for dairy cows. Biosystems Engineering, 169, 22–31.
   Web of Science ®Google Scholar
• Rao, Z. H., Zeng, C. Y., Wu, M. H., Wang, Z. F., Zhan, N., Liu, M., & Wan, X. K. (2018). Research on a handwritten character recognition algorithm based on an extended nonlinear kernel residual network. KSII Transactions on Internet and Information Systems, 12(1), 413–435.
   Web of Science ®Google Scholar
• Schuler, M. S., & Rose, S. (2017). Targeted maximum likelihood estimation for causal inference in observational studies. American Journal of Epidemiology, 185(1), 65–73.
   PubMed Web of Science ®Google Scholar
• Soderstrom, T., & Soverini, U. (2017). Errors-in-variables identification using maximum likelihood estimation in the frequency domain. Automatica, 79, 131–143.
   Web of Science ®Google Scholar
• Tolic, I., Milicevic, K., Suvak, N., & Biondic, I. (2018). Non-linear least squares and maximum likelihood estimation of probability density function of cross-border transmission losses. IEEE Transactions on Power Systems, 33(2), 2230–2238.
   Web of Science ®Google Scholar
• Wang, Y. J., & Ding, F. (2017). A filtering based multi-innovation gradient estimation algorithm and performance analysis for nonlinear dynamical systems. IMA Journal of Applied Mathematics, 82(6), 1171–1191.
   Web of Science ®Google Scholar
• Wang, Y. J., Ding, F., & Wu, M. H. (2018). Recursive parameter estimation algorithm for multivariate output-error systems. Journal of the Franklin Institute, 355(12), 5163–5181.
   Web of Science ®Google Scholar
• Wang, Y. J., Ding, F., & Xu, L. (2018). Some new results of designing an IIR filter with colored noise for signal processing. Digital Signal Processing, 72, 44–58.
   Web of Science ®Google Scholar
• Wang, T., Liu, L., Zhang, J., Schaeffer, E., & Wang, Y. (2019). A M-EKF fault detection strategy of insulation system for marine current turbine. Mechanical Systems and Signal Processing, 115, 269–280.
   Web of Science ®Google Scholar
• Wang, Y., Si, Y., Huang, B., & Ding, S. X. (2018). Survey on the theoretical research and engineering applications of multivariate statistics process monitoring algorithms: 2008-2017. The Canadian Journal of Chemical Engineering, 96(10), 2073–2085.
   Web of Science ®Google Scholar
• Wang, D. Q., Yan, Y. R., Liu, Y. J., & Ding, J. H. (2019). Model recovery for Hammerstein systems using the hierarchical orthogonal matching pursuit method. Journal of Computational and Applied Mathematics, 345, 135–145.
   Web of Science ®Google Scholar
• Wang, D. Q., Zhang, Z., & Yuan, J. Y. (2017). Maximum likelihood estimation method for dual-rate Hammerstein systems. International Journal of Control Automation and Systems, 15(2), 698–705.
   Web of Science ®Google Scholar
• Xu, L. (2014). A proportional differential control method for a time-delay system using the Taylor expansion approximation. Applied Mathematics and Computation, 236, 391–399.
   Web of Science ®Google Scholar
• Xu, L. (2015). Application of the Newton iteration algorithm to the parameter estimation for dynamical systems. Journal of Computational and Applied Mathematics, 288, 33–43.
   Web of Science ®Google Scholar
• Xu, L. (2017). The parameter estimation algorithms based on the dynamical response measurement data. Advances in Mechanical Engineering, 9(11), 1–12.
   Web of Science ®Google Scholar
• Xu, L., Chen, L., & Xiong, W. L. (2015). Parameter estimation and controller design for dynamic systems from the step responses based on the Newton iteration. Nonlinear Dynamics, 79(3), 2155–2163.
   Web of Science ®Google Scholar
• Xu, L., & Ding, F. (2017). The parameter estimation algorithms for dynamical response signals based on the multi-innovation theory and the hierarchical principle. IET Signal Processing, 11(2), 228–237.
   Web of Science ®Google Scholar
• Xu, L., & Ding, F. (2017). Parameter estimation for control systems based on impulse responses. International Journal of Control Automation and Systems, 15(6), 2471–2479.
   Web of Science ®Google Scholar
• Xu, L., & Ding, F. (2018). Iterative parameter estimation for signal models based on measured data. Circuits Systems and Signal Processing, 37(7), 3046–3069.
   Web of Science ®Google Scholar
• Xu, H., Ding, F., & Yang, E. F. (2019). Modeling a nonlinear process using the exponential autoregressive time series model. Nonlinear Dynamics. doi:10.1007/s11071-018-4677-0
   PubMed Web of Science ®Google Scholar
• Xu, L., Ding, F., & Zhu, Q. M. (2019). Hierarchical Newton and least squares iterative estimation algorithm for dynamic systems by transfer functions based on the impulse responses. International Journal of Systems Science, 50(1), 141–151.
   Web of Science ®Google Scholar
• Xu, G. H., Shekofteh, Y., Akgul, A., Li, C. B., & Panahi, S. (2018). A new chaotic system with a self-excited attractor: Entropy measurement, signal encryption, and parameter estimation. Entropy, 20(2). Article Number: 86. doi:10.3390/e20020086
   PubMed Web of Science ®Google Scholar
• Xu, L., Xiong, W. L., Alsaedi, A., & Hayat, T. (2018). Hierarchical parameter estimation for the frequency response based on the dynamical window data. International Journal of Control Automation and Systems, 16(4), 1756–1764.
   Web of Science ®Google Scholar
• Young, P. C. (2015). Refined instrumental variable estimation: Maximum likelihood optimization of a unified Box-Jenkins model. Automatica, 52, 35–46.
   Web of Science ®Google Scholar
• Yu, C. P., Ljung, L., & Verhaegen, M. (2018). Identification of structured state-space models. Automatica, 90, 54–61.
   Web of Science ®Google Scholar
• Yu, C. P., & Verhaegen, M. (2018). Subspace identification of individual systems operating in a network (SI2ON). IEEE Transactions on Automatic Control, 63(4), 1120–1125.
   Web of Science ®Google Scholar
• Zhan, X. S., Cheng, L. L., Wu, J., Yang, Q. S., & Han, T. (2019). Optimal modified performance of MIMO networked control systems with multi-parameter constraints. ISA Transactions, 84(1), 111–117.
   PubMedGoogle Scholar
• Zhang, Y. Z., Cao, Y., Wen, Y. H., Liang, L., & Zou, F. (2018). Optimization of information interaction protocols in cooperative vehicle-infrastructure systems. Chinese Journal of Electronics, 27(2), 439–444.
   Web of Science ®Google Scholar
• Zhang, X., Ding, F., Xu, L., & Yang, E. (2018). State filtering-based least squares parameter estimation for bilinear systems using the hierarchical identification principle. IET Control Theory Applications, 12(12), 1704–1713.
   Web of Science ®Google Scholar
• Zhang, X., Xu, L., Ding, F., & Hayat, T. (2018). Combined state and parameter estimation for a bilinear state space system with moving average noise. Journal of the Franklin Institute, 355(6), 3079–3103.
   Web of Science ®Google Scholar
• Zhang, W. H., Xue, L., & Jiang, X. (2018). Global stabilization for a class of stochastic nonlinear systems with SISS-like conditions and time delay. International Journal of Robust and Nonlinear Control, 28(13), 3909–3926.
   Web of Science ®Google Scholar
• Zhao, S. Y., Huang, B., & Liu, F. (2017). Detection and diagnosis of multiple faults with uncertain modeling parameters. IEEE Transactions on Control Systems Technology, 25(5), 1873–1881.
   Web of Science ®Google Scholar
• Zhao, S. Y., Huang, B., & Liu, F. (2017). Linear optimal unbiased filter for time-variant systems without apriori information on initial conditions. IEEE Transactions on Automatic Control, 62(2), 882–887.
   Web of Science ®Google Scholar
• Zhao, N., Liu, R., Chen, Y., Wu, M., Jiang, Y., Xiong, W., & Liu, C. (2018). Contract design for relay incentive mechanism under dual asymmetric information in cooperative networks. Wireless Networks, 24(8), 3029–3044.
   Web of Science ®Google Scholar
• Zhao, S. Y., Shmaliy, Y. S., & Ahn, C. K. (2018). Bias-constrained optimal fusion filtering for decentralized WSN with correlated noise sources. IEEE Transactions on Signal and Information Processing over Networks, 4(4), 727–735.
   Web of Science ®Google Scholar
• Zhao, S. Y., Shmaliy, Y. S., Ahn, C. K., & Liu, F. (2018). Adaptive-horizon iterative UFIR filtering algorithm with applications. IEEE Transactions on Industrial Electronics, 65(8), 6393–6402.
   Web of Science ®Google Scholar
• Zhao, S. Y., Shmaliy, Y. S., Shi, P., & Ahn, C. K. (2017). Fusion Kalman/UFIR filter for state estimation with uncertain parameters and noise statistics. IEEE Transactions on Industrial Electronics, 64(4), 3075–3083.
   Web of Science ®Google Scholar