In this paper, a novel identification method is addressed for a class of nonlinear Wiener systems subject to time delay, and this identification problem involves the estimation of delay time, transfer function model parameters and neural fuzzy model parameters. Aiming to identify separately the linear and nonlinear blocks, the separable signals are introduced. First, the correlation characteristics of separable signals through the Wiener system are analyzed, then the correlation analysis technique is applied to calculate the unknown parameters involving time delay and transfer function model. Moreover, in the neural fuzzy model parameters estimation, we first calculate the center and width of the neural fuzzy model. Then, to improve global search mechanism ability and converge speed of particle swarm optimization method, the improved particle swarm optimization and cuckoo search techniques are introduced to figure out the weight of the neural fuzzy model, which obtains good global search ability and convergence speed. The simulation comparison results in numerical case and nonlinear process are presented to verify that the feasibility of the Wiener system identification.
AtitallahABedouiSAbderrahimK (2015) Identification of Wiener time delay systems based on hierarchical gradient approach. IFAC-PapersOnLine48(1): 403–408.
2.
BrouriAKadiLLahdachiK (2022) Identification of nonlinear system composed of parallel coupling of Wiener and Hammerstein models. Asian Journal of Control24(3): 1152–1164.
3.
DingFLiuXLiuM (2016) The recursive least squares identification algorithm for a class of Wiener nonlinear systems. Journal of the Franklin Institute353(7): 1518–1526.
4.
DingFXuLZhangX, et al. (2023) Filtered auxiliary model recursive generalized extended parameter estimation methods for Box-Jenkins systems by means of the filtering identification idea. International Journal of Robust Nonlinear Control33(10): 5510–5535.
5.
DingFXuLZhangX, et al. (2024) Recursive identification methods for general stochastic systems with colored noises by using the hierarchical identification principle and the filtering identification idea. Annual Reviews in Control57: 1100942.
6.
El-NagarAKhalifaTEl-BrawanyM, et al. (2022) Adaptive interval type-2 fuzzy controller for nonlinear networked Wiener systems subject to packet dropout and time-varying delay. ISA Transactions128: 565–580.
7.
HouJ (2024) Parsimonious model based consistent subspace identification of Hammerstein systems under periodic disturbances. International Journal of Control, Automation and Systems22(1): 61–71.
8.
JiYZhangCKangZ, et al. (2020) Parameter estimation for block-oriented nonlinear systems using the key term separation. International Journal of Robust Nonlinear Control30(9): 3727–3752.
9.
JinQWangZCaiW, et al. (2021) Parameter identification for Wiener-finite impulse response system with output data of missing completely at random mechanism and time delay. International Journal of Adaptive Control and Signal Processing35(5): 811–827.
10.
KazemiMArefiMAlipouriY (2019) Wiener model based GMVC design considering sensor noise and delay. ISA Transactions88: 73–81.
11.
KothariK (2024) Application of fractional calculus for parameter estimation of nonlinear Wiener systems with time delay. IEEE Access12: 26281–26294.
12.
LiFHanJ (2024) Parameters estimation for the Hammerstein-Wiener models with colored noise based on hybrid signals. International Journal of Adaptive Control and Signal Processing38(3): 921–937.
13.
LiFJiaLGuY (2023a) Identification of nonlinear process described by neural fuzzy Hammerstein-Wiener model using multi-signal processing. Advances in Manufacturing11(4): 694–707.
14.
LiFLiangMHeN, et al. (2023b) Separation identification approach for the Hammerstein-Wiener nonlinear systems with process noise using correlation analysis. International Journal of Robust Nonlinear Control33(14): 8105–8123.
15.
LiFQianSHeN, et al. (2024) Estimation of Wiener nonlinear systems with measurement noises utilizing correlation analysis and Kalman filter. International Journal of Robust and Nonlinear Control34(7): 4706–4718.
16.
LiFZhengTCaoQ (2023c) Modelling and identification for practical nonlinear process using neural fuzzy network-based Hammerstein system. Transactions of the Institute of Measurement and Control45(11): 2091–2102.
17.
LiJZongTLuG (2022) Parameter identification of Hammerstein-Wiener nonlinear systems with unknown time delay based on the linear variable weight particle swarm optimization. ISA Transactions120: 89–98.
18.
LiJZongTGuJ, et al. (2020) Parameter estimation of Wiener systems based on the particle swarm iteration and gradient search principle. Circuits, Systems, and Signal Processing39(7): 3470–3495.
19.
LiangMWangLWangL, et al. (2024) A hypervolume-based cuckoo search algorithm with enhanced diversity and adaptive scaling factor. Applied Soft Computing151: 111073.
20.
LiuQLinWJiangS, et al. (2020) Robust estimation of Wiener models in the presence of outliers using the VB approach. IEEE Transactions on Industrial Electronics68(11): 11390–11399.
21.
LyuBJiaLLiF (2020) Parameter estimation of neuro-fuzzy wiener model with colored noise using separable signals. IEEE Access88: 67047–67058.
22.
NadiMArefiM (2023) Hierarchical iterative identification of output nonlinear Box-Jenkins Wiener model with ARMA noise. ISA Transactions143: 321–333.
23.
SersourLDjamahTBettayebM (2015) Identification of Wiener fractional model using self-adaptive velocity particle swarm optimization. In: 2015 7th International Conference on Modelling, Identification and Control (ICMIC), Sousse, Tunisia, 2015. IEEE, pp. 1–6.
24.
TakagiTSugenoM (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics1: 116–132.
25.
TangYQiaoLGuanX (2010) Identification of Wiener model using step signals and particle swarm optimization. Expert Systems with Applications37(4): 3398–3404.
26.
VanVaerenberghSViaJSantamariaI (2013) Blind identification of SIMO Wiener systems based on kernel canonical correlation analysis. IEEE Transactions on Signal Processing61(9): 2219–2230.
27.
WangTTanYFangM (2022a) Stability of Wiener systems with dead-zone using optimal position and velocity control. International Journal of Control, Automation and Systems20(2): 403–410.
28.
WangZZhangYJinQ, et al. (2022b) Wiener models robust identification of multi-rate process with time-varying delay using expectation-maximization algorithm. Journal of Process Control118: 126–138.
29.
YangXXiongWMaJ, et al. (2017) Robust identification of Wiener time-delay system with expectation-maximization algorithm. Journal of the Franklin Institute354(13): 5678–5693.
30.
ZongTLiJGuopingG (2024) Parameter estimation of multivariable Wiener nonlinear systems by the improved particle swarm optimization and coupling identification. Information Sciences661: 120192.
