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Parameter estimation methods of linear continuous-time time-delay systems from multi-frequency response data. (English) Zbl 07919996

MSC:

93B30 System identification
93C05 Linear systems in control theory
93C43 Delay control/observation systems
Full Text: DOI

References:

[1] Cao, Y.; An, Y.; Su, S., A statistical study of railway safety in China and Japan 1990-2020, Accid. Anal. Prevent., 175 (2022) · doi:10.1016/j.aap.2022.106764
[2] Cao, Y.; Ji, Y.; Sun, Y., The fault diagnosis of a switch machine based on deep random forest fusion, IEEE Intell. Transp. Syst. Mag. (2023) · doi:10.1109/MITS.2022.3174238
[3] Cao, Y.; Ma, L.; Xiao, S., Standard analysis for transfer delay in CTCS-3, Chin. J. Electron., 26, 5, 1057-1063 (2017) · doi:10.1049/cje.2017.08.024
[4] Cao, Y.; Sun, Y.; Xie, G., A sound-based fault diagnosis method for railway point machines based on two-stage feature selection strategy and ensemble classifier, IEEE Trans. Intell. Transp. Syst. (2022) · doi:10.1109/TITS.2021.3109632
[5] Cao, Y.; Sun, Y.; Xie, G., Fault diagnosis of train plug door based on a hybrid criterion for IMFs selection and fractional wavelet package energy entropy, IEEE Trans. Veh. Technol., 68, 8, 7544-7551 (2019) · doi:10.1109/TVT.2019.2925903
[6] Cao, Y.; Wang, Z.; Liu, F., Bio-inspired speed curve optimization and sliding mode tracking control for subway trains, IEEE Trans. Veh. Technol., 68, 7, 6331-6342 (2019) · doi:10.1109/TVT.2019.2914936
[7] Cao, Y.; Wen, J.; Hobiny, A., Parameter-varying artificial potential field control of virtual coupling system with nonlinear dynamics, Fractals, 30, 2, 2240099 (2022) · Zbl 1485.93023 · doi:10.1142/S0218348X22400990
[8] Cao, Y.; Wen, J.; Ma, L., Tracking and collision avoidance of virtual coupling train control system, Alex. Eng. J., 60, 2, 2115-2125 (2021) · doi:10.1016/j.aej.2020.12.010
[9] Cao, Y.; Yang, Y.; Ma, L., Research on virtual coupled train control method based on GPC & VAPF, Chin. J. Electron., 31, 5, 897-905 (2022) · doi:10.1049/cje.2021.00.241
[10] Cao, Y.; Zhang, ZX; Cheng, F., Trajectory optimization for high-speed trains via a mixed integer linear programming approach, IEEE Trans. Intell. Transp. Syst. (2023) · doi:10.1109/TITS.2022.3155628
[11] Chen, K.; Ai, W.; Chen, B., Mechanical parameter identification of two-mass drive system based on variable forgetting factor recursive least squares method, Trans. Inst. Meas. Control., 41, 2, 1-10 (2018)
[12] Chen, F.; Garnier, H.; Gilson, M., Robust identification of continuous-time models with arbitrary time-delay from irregularly sampled data, J. Process Control, 25, 19-27 (2015) · doi:10.1016/j.jprocont.2014.10.003
[13] Chen, F.; Garnier, H.; Padilla, A., Recursive IV identification of continuous-time models with time delay from sampled data, IEEE Trans. Control Syst. Technol., 28, 3, 1074-1082 (2020) · doi:10.1109/TCST.2019.2896124
[14] Chen, Y.; Zhang, C.; Liu, C., Atrial fibrillation detection using feedforward neural network, J. Med. Biol. Eng., 42, 1, 63-73 (2022) · doi:10.1007/s40846-022-00681-z
[15] Cui, T.; Ding, F.; Hayat, T., Moving data window-based partially-coupled estimation approach for modeling a dynamical system involving unmeasurable states, ISA Trans. Part B, 128, 437-452 (2022) · doi:10.1016/j.isatra.2021.11.011
[16] Ding, F.; Lv, L.; Pan, J., Two-stage gradient-based iterative estimation methods for controlled autoregressive systems using the measurement data, Int. J. Control Autom. Syst., 18, 4, 886-896 (2020) · doi:10.1007/s12555-019-0140-3
[17] Ding, F.; Ma, H.; Pan, J., Hierarchical gradient- and least squares-based iterative algorithms for input nonlinear output-error systems using the key term separation, J. Franklin Inst., 358, 9, 5113-5135 (2021) · Zbl 1465.93040 · doi:10.1016/j.jfranklin.2021.04.006
[18] Ding, F.; Wang, F.; Xu, L., Decomposition based least squares iterative identification algorithm for multivariate pseudo-linear ARMA systems using the data filtering, J. Franklin Inst., 354, 3, 1321-1339 (2017) · Zbl 1355.93190 · doi:10.1016/j.jfranklin.2016.11.030
[19] Ding, JL; Zhang, W., Finite-time adaptive control for nonlinear systems with uncertain parameters based on the command filters, Int. J. Adapt. Control Signal Process., 35, 9, 1754-1767 (2021) · Zbl 1543.93146 · doi:10.1002/acs.3287
[20] Dong, S.; Liu, T.; Wang, W., Identification of discrete-time output error model for industrial processes with time delay subject to load disturbance, J. Process Control, 50, 40-55 (2017) · doi:10.1016/j.jprocont.2016.11.007
[21] Fan, Y.; Liu, X., Auxiliary model-based multi-innovation recursive identification algorithms for an input nonlinear controlled autoregressive moving average system with variable-gain nonlinearity, Int. J. Adapt. Control Signal Process., 36, 3, 690-707 (2022) · Zbl 07841301 · doi:10.1002/acs.3354
[22] Geng, FZ; Wu, XY, A novel kernel functions algorithm for solving impulsive boundary value problems, Appl. Math. Lett., 134 (2022) · Zbl 1503.65151 · doi:10.1016/j.aml.2022.108318
[23] Gu, Y.; Zhu, QM; Nouri, H., Identification and U-control of a state-space system with time-delay, Int. J. Adapt. Control Signal Process., 36, 1, 138-154 (2022) · Zbl 1543.93233 · doi:10.1002/acs.3345
[24] Ha, H.; Welsh, JS; Alamir, M., Useful redundancy in parameter and time delay estimation for continuous-time models, Automatica, 95, 455-462 (2018) · Zbl 1402.93092 · doi:10.1016/j.automatica.2018.06.023
[25] Hou, J.; Chen, F.; Li, P., Gray-box parsimonious subspace identification of Hammerstein-type systems, IEEE Trans. Ind. Electron., 68, 10, 9941-9951 (2021) · doi:10.1109/TIE.2020.3026286
[26] Hou, J.; Su, H.; Yu, C., Bias-correction errors-in-variables Hammerstein model identification, IEEE Trans. Ind. Electron. (2022) · doi:10.1109/TIE.2022.3199931
[27] Hou, J.; Su, H.; Yu, C., Consistent subspace identification of errors-in-variables Hammerstein systems, IEEE Trans. Syst. Man Cybern. Syst. (2022) · doi:10.1109/TSMC.2022.3213809
[28] Hwang, JK; Liu, Y., Identification of interarea modes from an effectual impulse response of ringdown frequency data, Electr. Power Syst. Res., 144, 96-106 (2017) · doi:10.1016/j.epsr.2016.11.019
[29] Ji, Y.; Jiang, XK; Wan, LJ, Hierarchical least squares parameter estimation algorithm for two-input Hammerstein finite impulse response systems, J. Franklin Inst., 357, 8, 5019-5032 (2020) · Zbl 1437.93131 · doi:10.1016/j.jfranklin.2020.03.027
[30] Ji, Y.; Kang, Z., Three-stage forgetting factor stochastic gradient parameter estimation methods for a class of nonlinear systems, Int. J. Robust Nonlinear Control, 31, 3, 971-987 (2021) · Zbl 1525.93438 · doi:10.1002/rnc.5323
[31] Ji, Y.; Kang, Z.; Liu, XM, The data filtering based multiple-stage Levenberg-Marquardt algorithm for Hammerstein nonlinear systems, Int. J. Robust Nonlinear Control, 31, 15, 7007-7025 (2021) · Zbl 1527.93457 · doi:10.1002/rnc.5675
[32] Ji, Y.; Kang, Z.; Zhang, C., Two-stage gradient-based recursive estimation for nonlinear models by using the data filtering, Int. J. Control Autom. Syst., 19, 8, 2706-2715 (2021) · doi:10.1007/s12555-019-1060-y
[33] Ji, Y.; Zhang, C.; Kang, Z.; Yu, T., Parameter estimation for block-oriented nonlinear systems using the key term separation, Int. J. Robust Nonlinear Control, 30, 9, 3727-3752 (2020) · Zbl 1466.93161 · doi:10.1002/rnc.4961
[34] Kang, Z.; Ji, Y.; Liu, XM, Hierarchical recursive least squares algorithms for Hammerstein nonlinear autoregressive output-error systems, Int. J. Adapt. Control Signal Process., 35, 11, 2276-2295 (2021) · Zbl 1543.93319 · doi:10.1002/acs.3320
[35] Li, JM; Ding, F.; Hayat, T., A novel nonlinear optimization method for fitting a noisy Gaussian activation function, Int. J. Adapt. Control Signal Process., 36, 3, 690-707 (2022) · Zbl 07841310 · doi:10.1002/acs.3367
[36] Li, MH; Liu, XM, Maximum likelihood hierarchical least squares-based iterative identification for dual-rate stochastic systems, Int. J. Adapt. Control Signal Process., 35, 2, 240-261 (2021) · Zbl 1543.93353 · doi:10.1002/acs.3203
[37] Li, MH; Liu, XM, Iterative identification methods for a class of bilinear systems by using the particle filtering technique, Int. J. Adapt. Control Signal Process., 35, 10, 2056-2074 (2021) · Zbl 1543.93320 · doi:10.1002/acs.3308
[38] Li, MH; Liu, XM, Particle filtering-based iterative identification methods for a class of nonlinear systems with interval-varying measurements, Int. J. Control Autom. Syst., 20, 7, 2239-2248 (2022) · doi:10.1007/s12555-021-0448-7
[39] Li, MH; Liu, XM, The filtering-based maximum likelihood iterative estimation algorithms for a special class of nonlinear systems with autoregressive moving average noise using the hierarchical identification principle, Int. J. Adapt. Control Signal Process., 33, 7, 1189-1211 (2019) · Zbl 1425.93284 · doi:10.1002/acs.3029
[40] Li, J.; Ma, D., Laplace transforms and valuations, J. Funct. Anal., 272, 2, 739-758 (2017) · Zbl 1353.44001 · doi:10.1016/j.jfa.2016.09.011
[41] Li, M.; Xu, G.; Lai, Q.; Chen, J., A chaotic strategy-based quadratic opposition-based learning adaptive variable-speed whale optimization algorithm, Math. Comput. Simul., 193, 71-99 (2022) · Zbl 1540.90308 · doi:10.1016/j.matcom.2021.10.003
[42] Li, M.; Xu, G.; Zeng, L., Hybrid whale optimization algorithm based on symbiosis strategy for global optimization, Appl. Intell. (2022) · doi:10.1007/s10489-022-04132-9
[43] Li, JH; Zong, TC; Lu, GP, Parameter identification of Hammerstein-Wiener nonlinear systems with unknown time delay based on the linear variable weight particle swarm optimization, ISA Trans., 120, 89-98 (2022) · doi:10.1016/j.isatra.2021.03.021
[44] Liu, M.; Chen, H., H-infinity state estimation for discrete-time delayed systems of the neural network type with multiple missing measurements, IEEE Trans. Neural Netw. Learn. Syst., 26, 12, 2987-2998 (2015) · doi:10.1109/TNNLS.2015.2399331
[45] Liu, Q.; Ding, F., Gradient-based recursive parameter estimation for a periodically nonuniformly sampled-data Hammerstein-Wiener system based on the key-term separation, Int. J. Adapt. Control Signal Process., 35, 10, 1970-1989 (2021) · Zbl 1543.93323 · doi:10.1002/acs.3296
[46] Liu, Q.; Chen, F.; Hayat, T., Recursive least squares estimation methods for a class of nonlinear systems based on non-uniform sampling, Int. J. Adapt. Control Signal Process., 35, 8, 1612-1632 (2021) · Zbl 1543.93322 · doi:10.1002/acs.3263
[47] Liu, SY; Ding, F.; Xu, L., Hierarchical principle-based iterative parameter estimation algorithm for dual-frequency signals, Circuits Syst Signal Process., 38, 7, 3251-3268 (2019) · doi:10.1007/s00034-018-1015-1
[48] Liu, Q.; Shang, C.; Huang, D., Efficient low-order system identification from low-quality step response data with rank-constrained optimization, Control. Eng. Pract., 107 (2021) · doi:10.1016/j.conengprac.2020.104671
[49] Liu, T.; Tian, H.; Rong, S., Heating-up control with delay-free output prediction for industrial jacketed reactors based on step response identification, ISA Trans., 83, 227-238 (2018) · doi:10.1016/j.isatra.2018.09.001
[50] Liu, H.; Wang, J.; Ji, Y., Maximum likelihood recursive generalized extended least squares estimation methods for a bilinear-parameter systems with ARMA noise based on the over-parameterization model, Int. J. Control Autom. Syst., 20, 8, 2606-2615 (2022) · doi:10.1007/s12555-021-0367-7
[51] Ma, P.; Ding, F., New gradient based identification methods for multivariate pseudo-linear systems using the multi-innovation and the data filtering, J. Franklin Inst., 354, 3, 1568-1583 (2017) · Zbl 1355.93194 · doi:10.1016/j.jfranklin.2016.11.025
[52] Ma, J.; Ding, F., Filtering-based multistage recursive identification algorithm for an input nonlinear output-error autoregressive system by using the key term separation technique, Circuits Syst. Signal Process., 36, 2, 577-599 (2017) · Zbl 1368.93724 · doi:10.1007/s00034-016-0333-4
[53] Ma, H.; Pan, J.; Ding, W., Partially-coupled least squares based iterative parameter estimation for multi-variable output-error-like autoregressive moving average systems, IET Control Theory Appl., 13, 18, 3040-3051 (2019) · doi:10.1049/iet-cta.2019.0112
[54] Ma, P.; Wang, L., Filtering-based recursive least squares estimation approaches for multivariate equation-error systems by using the multiinnovation theory, Int. J. Adapt. Control Signal Process., 35, 9, 1898-1915 (2021) · Zbl 1536.93999 · doi:10.1002/acs.3302
[55] Ma, J.; Xiong, W.; Chen, J., Hierarchical identification for multivariate Hammerstein systems by using the modified Kalman filter, IET Control Theory Appl., 11, 6, 857-869 (2017) · doi:10.1049/iet-cta.2016.1033
[56] Meng, X.; Ji, Y.; Wang, J., Iterative parameter estimation for photovoltaic cell models by using the hierarchical principle, Int. J. Control Autom. Syst., 20, 8, 2583-2593 (2022) · doi:10.1007/s12555-021-0588-9
[57] Ni, JY; Xu, L.; Hayat, T., Parameter estimation for time-delay systems based on the frequency responses and harmonic balance methods, Int. J. Adapt. Control Signal Process., 34, 12, 1779-1798 (2020) · Zbl 1543.93051 · doi:10.1002/acs.3180
[58] Ni, J.; Zhang, Y.; Hayat, T., Parameter estimation algorithms of linear systems with time-delays based on the frequency responses and harmonic balances under the multi-frequency sinusoidal signal excitation, Signal Process., 181 (2021) · doi:10.1016/j.sigpro.2020.107904
[59] Pan, J.; Chen, Q.; Xiong, J.; Chen, G., A novel quadruple boost nine level switched capacitor inverter, J. Electr. Eng. Technol. (2022) · doi:10.1007/s42835-022-01130-2
[60] Pan, J.; Jiang, X.; Wan, X., A filtering based multi-innovation extended stochastic gradient algorithm for multivariable control systems, Int. J. Control Autom. Syst., 15, 3, 1189-1197 (2017) · doi:10.1007/s12555-016-0081-z
[61] Pan, J.; Li, W.; Zhang, HP, Control algorithms of magnetic suspension systems based on the improved double exponential reaching law of sliding mode control, Int. J. Control Autom. Syst., 16, 6, 2878-2887 (2018) · doi:10.1007/s12555-017-0616-y
[62] Pan, J.; Liu, S.; Shu, J., Hierarchical recursive least squares estimation algorithm for secondorder Volterra nonlinear systems, Int. J. Control Autom. Syst., 20, 12, 3940-3950 (2022) · doi:10.1007/s12555-021-0845-y
[63] Pan, J.; Ma, H.; Zhang, X., Recursive coupled projection algorithms for multivariable output-error-like systems with coloured noises, IET Signal Process., 14, 7, 455-466 (2020) · doi:10.1049/iet-spr.2019.0481
[64] Pouliquen, M.; Pigeon, E.; Gehan, O., Impulse response identification from input/output binary measurements, Automatica, 123 (2021) · Zbl 1461.93101 · doi:10.1016/j.automatica.2020.109307
[65] Ren, X.; Rad, A.; Chan, P.; Lo, W., Online identification of continuous-time systems with unknown time delay, IEEE Trans. Autom. Control, 50, 9, 1418-1422 (2005) · Zbl 1365.93108 · doi:10.1109/TAC.2005.854640
[66] So, HC, Noisy input-output system identification approach for time delay estimation, Signal Process., 82, 10, 1471-1475 (2002) · Zbl 1080.93673 · doi:10.1016/S0165-1684(02)00289-X
[67] Su, S.; She, J.; Li, K., A nonlinear safety equilibrium spacing based model predictive control for virtually coupled train set over gradient terrains, IEEE Trans. Transp. Electrif., 8, 2, 2810-2824 (2022) · doi:10.1109/TTE.2021.3134669
[68] Su, S.; Tang, T.; Xun, J., Design of running grades for energy-efficient train regulation: a case study for Beijing Yizhuang line, IEEE Intell. Transp. Syst. Mag., 13, 2, 189-200 (2021) · doi:10.1109/MITS.2019.2907681
[69] Su, S.; Wang, X.; Cao, Y.; Yin, JT, An energy-efficient train operation approach by integrating the metro timetabling and eco-driving, IEEE Trans. Intell. Transp. Syst., 21, 10, 4252-4268 (2020) · doi:10.1109/TITS.2019.2939358
[70] Su, S.; Wang, X.; Tang, T., Energy-efficient operation by cooperative control among trains: A multi-agent reinforcement learning approach, Control Eng. Pract., 116 (2021) · doi:10.1016/j.conengprac.2021.104901
[71] Su, S.; Zhu, Q.; Liu, J., Eco-driving of trains with a data-driven iterative learning approach, IEEE Trans. Ind. Inf. (2023) · doi:10.1109/TII.2022.3195888
[72] Sun, YK; Cao, Y.; Li, P., Contactless fault diagnosis for railway point machines based on multi-scale fractional wavelet packet energy entropy and synchronous optimization strategy, IEEE Trans. Veh. Technol., 71, 6, 5906-5914 (2022) · doi:10.1109/TVT.2022.3158436
[73] Sun, YK; Cao, Y.; Ma, LC, A fault diagnosis method for train plug doors via sound signals, IEEE Intell. Transp. Syst. Mag., 13, 3, 107-117 (2021) · doi:10.1109/MITS.2019.2926366
[74] Sun, YK; Cao, Y.; Xie, G.; Wen, T., Sound based fault diagnosis for RPMs based on multi-scale fractional permutation entropy and two-scale algorithm, IEEE Trans. Veh. Technol., 70, 11, 11184-11192 (2021) · doi:10.1109/TVT.2021.3090419
[75] Voros, J., Identification of Hammerstein systems with time-varying piecewise-linear characteristics, IEEE Trans. Circuits Syst. II Express Briefs, 52, 12, 865-869 (2005) · doi:10.1109/TCSII.2005.853339
[76] Wan, LJ; Ding, F., Decomposition- and gradient-based iterative identification algorithms for multivariable systems using the multi-innovation theory, Circuits Syst. Signal Process., 38, 7, 2971-2991 (2019) · doi:10.1007/s00034-018-1014-2
[77] Wang, X.; Ding, F., Modified particle filtering-based robust estimation for a networked control system corrupted by impulsive noise, Int. J. Robust Nonlinear Control, 32, 2, 830-850 (2022) · Zbl 1527.93452 · doi:10.1002/rnc.5850
[78] Wang, Y.; Ding, F.; Wu, MH, Recursive parameter estimation algorithm for multivariate output-error systems, J. Franklin Inst., 355, 12, 5163-5181 (2018) · Zbl 1395.93288 · doi:10.1016/j.jfranklin.2018.04.013
[79] Wang, J.; Ding, C.; Wu, M., Lightweight multiple scale-patch dehazing network for real-world hazy image, KSII Trans. Int. Inf. Syst., 15, 12, 4420-4438 (2022)
[80] Wang, H.; Fan, H.; Pan, J., A true three-scroll chaotic attractor coined, Discrete Contin. Dyn. Syst. Ser. B, 27, 5, 2891-2915 (2022) · Zbl 1495.34025 · doi:10.3934/dcdsb.2021165
[81] Wang, J.; Ji, Y.; Zhang, C., Iterative parameter and order identification for fractional-order nonlinear finite impulse response systems using the key term separation, Int. J. Adapt. Control Signal Process., 35, 8, 1562-1577 (2021) · Zbl 1543.93052 · doi:10.1002/acs.3257
[82] Wang, J.; Ji, Y.; Zhang, X., Two-stage gradient-based iterative algorithms for the fractional-order nonlinear systems by using the hierarchical identification principle, Int. J. Adapt. Control Signal Process., 36, 7, 1778-1796 (2022) · Zbl 07841875 · doi:10.1002/acs.3420
[83] Wang, X.; Su, S.; Cao, Y., Robust control for dynamic train regulation in fully automatic operation system under uncertain wireless transmissions, IEEE Trans. Intell. Transp. Syst. (2022) · doi:10.1109/TITS.2022.3170950
[84] Wang, Y.; Tang, S.; Deng, M., Modeling nonlinear systems using the tensor network B-spline and the multi-innovation identification theory, Int. J. Robust Nonlinear Control, 32, 13, 7304-7318 (2022) · Zbl 1528.93083 · doi:10.1002/rnc.6221
[85] Wang, Y.; Tang, S.; Gu, X., Parameter estimation for nonlinear Volterra systems by using the multi-innovation identification theory and tensor decomposition, J. Franklin Inst., 359, 2, 1782-1802 (2022) · Zbl 1481.93022 · doi:10.1016/j.jfranklin.2021.11.015
[86] Wang, Y.; Yang, L., An efficient recursive identification algorithm for multilinear systems based on tensor decomposition, Int. J. Robust Nonlinear Control, 31, 16, 7920-7936 (2021) · Zbl 1527.93044 · doi:10.1002/rnc.5718
[87] Wang, Y.; Yang, G., Arrhythmia classification algorithm based on multi-head self-attention mechanism, Biomed. Signal Process. Control, 79 (2023) · doi:10.1016/j.bspc.2022.104206
[88] Wei, C.; Zhang, X.; Xu, L., Overall recursive least squares and overall stochastic gradient algorithms and their convergence for feedback nonlinear controlled autoregressive systems, Int. J. Robust Nonlinear Control, 32, 9, 5534-5554 (2022) · Zbl 1528.93227 · doi:10.1002/rnc.6101
[89] Xiong, J.; Pan, J.; Chen, G., Sliding mode dual-channel disturbance rejection attitude control for a quadrotor, IEEE Trans. Ind. Electron., 69, 10, 10489-10499 (2022) · doi:10.1109/TIE.2021.3137600
[90] Xu, L., Separable multi-innovation Newton iterative modeling algorithm for multi-frequency signals based on the sliding measurement window, Circuits Syst. Signal Process., 41, 2, 805-830 (2022) · Zbl 1509.94036 · doi:10.1007/s00034-021-01801-x
[91] Xu, L., Separable Newton recursive estimation method through system responses based on dynamically discrete measurements with increasing data length, Int. J. Control Autom. Syst., 20, 2, 432-443 (2022) · doi:10.1007/s12555-020-0619-y
[92] Xu, L.; Chen, FY; Hayat, T., Hierarchical recursive signal modeling for multi-frequency signals based on discrete measured data, Int. J. Adapt. Control Signal Process., 35, 5, 676-693 (2021) · Zbl 1543.93331 · doi:10.1002/acs.3221
[93] Xu, L.; Ding, F.; Wan, L.; Sheng, J., Separable multi-innovation stochastic gradient estimation algorithm for the nonlinear dynamic responses of systems, Int. J. Adapt. Control Signal Process., 34, 7, 937-954 (2020) · Zbl 1469.93111 · doi:10.1002/acs.3113
[94] Xu, L.; Ding, F.; Yang, E., Auxiliary model multiinnovation stochastic gradient parameter estimation methods for nonlinear sandwich systems, Int. J. Robust Nonlinear Control, 31, 1, 148-165 (2021) · Zbl 1525.93043 · doi:10.1002/rnc.5266
[95] Xu, L.; Ding, F.; Zhu, Q., Separable synchronous multi-innovation gradient-based iterative signal modeling from on-line measurements, IEEE Trans. Instrum. Meas., 71, 6501313 (2022)
[96] Xu, L.; Ding, F.; Zhu, Q., Decomposition strategy-based hierarchical least mean square algorithm for control systems from the impulse responses, Int. J. Syst. Sci., 52, 9, 1806-1821 (2021) · Zbl 1483.93637 · doi:10.1080/00207721.2020.1871107
[97] Xu, L.; Song, GL, A recursive parameter estimation algorithm for modeling signals with multi-frequencies, Circuits Syst. Signal Process., 39, 8, 4198-4224 (2020) · Zbl 1452.94026 · doi:10.1007/s00034-020-01356-3
[98] Xu, C.; Xu, H.; Guan, Z., Observer-based dynamic event-triggered semi-global bipartite consensus of linear multi-agent systems with input saturation, IEEE Trans. Cybern. (2023) · doi:10.1109/TCYB.2022.3164048
[99] Yang, X.; Xiong, WL; Ma, JX, Robust identification of Wiener time-delay system with expectation-maximization algorithm, J. Franklin Inst., 354, 13, 5678-5693 (2017) · Zbl 1395.93262 · doi:10.1016/j.jfranklin.2017.05.023
[100] Yao, W.; Marques, S., Prediction of transonic limit-cycle oscillations using an aeroelastic harmonic balance method, AIAA J., 53, 7, 2040-2051 (2015) · doi:10.2514/1.J053565
[101] Yin, CC; Wen, YZ, An extension of Paulsen-Gjessing’s risk model with stochastic return on investments, Insur. Math. Econom., 52, 3, 469-476 (2013) · Zbl 1284.91281 · doi:10.1016/j.insmatheco.2013.02.014
[102] Yin, CC; Zhao, JS, Nonexponential asymptotics for the solutions of renewal equations, with applications, J. Appl. Probab., 43, 3, 815-824 (2006) · Zbl 1125.60090 · doi:10.1239/jap/1158784948
[103] Yin, CC; Yuen, KC, Optimality of the threshold dividend strategy for the compound Poisson model, Stat. Probab. Lett., 81, 12, 1841-1846 (2011) · Zbl 1225.91030 · doi:10.1016/j.spl.2011.07.022
[104] Yin, CC; Yuen, KC, Optimal dividend problems for a jump-diffusion model with capital injections and proportional transaction costs, J. Ind. Manag. Optim., 11, 4, 1247-1262 (2015) · Zbl 1328.93285 · doi:10.3934/jimo.2015.11.1247
[105] You, J.; Yu, C.; Sun, J., Generalized maximum entropy based identification of graphical ARMA models, Automatica, 141 (2022) · Zbl 1491.93030 · doi:10.1016/j.automatica.2022.110319
[106] Yu, C.; Li, Y.; Fang, H., System identification approach for inverse optimal control of finite-horizon linear quadratic regulators, Automatica, 129 (2021) · Zbl 1478.93129 · doi:10.1016/j.automatica.2021.109636
[107] Zhang, X.; Ding, F., Hierarchical parameter and state estimation for bilinear systems, Int. J. Syst. Sci., 51, 2, 275-290 (2020) · Zbl 1483.93677 · doi:10.1080/00207721.2019.1704093
[108] Zhang, XX; Ji, JC; Xu, J., Parameter identification of time-delayed nonlinear systems: an integrated method with adaptive noise correction, J. Franklin Inst., 356, 11, 5858-5880 (2019) · Zbl 1415.93272 · doi:10.1016/j.jfranklin.2019.03.023
[109] Zhang, C.; Liu, HB; Ji, Y., Gradient parameter estimation of a class of nonlinear systems based on the maximum likelihood principle, Int. J. Control Autom. Syst., 20, 5, 1393-1404 (2022) · doi:10.1007/s12555-021-0249-z
[110] Zhang, XX; Xu, J., Identification of time delay in nonlinear systems with delayed feedback control, J. Franklin Inst., 352, 8, 298-2998 (2015) · Zbl 1395.93173 · doi:10.1016/j.jfranklin.2014.04.016
[111] Zhao, N.; Wu, A.; Pei, Y., Spatial-temporal aggregation graph convolution network for efficient mobile cellular traffic prediction, IEEE Commun. Lett., 26, 3, 587-591 (2022) · doi:10.1109/LCOMM.2021.3138075
[112] Zhu, W.; Wu, S.; Wang, X., Harmonic balance method implementation of onlinear dynamic characteristics for compound planetary gear sets, Nonlinear Dyn., 81, 3, 1511-1522 (2015) · Zbl 1348.85003 · doi:10.1007/s11071-015-2084-3
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