Bias compensation methods for stochastic systems with colored noise

Parameter estimation for an input nonlinear state space system with time delay

“…The gradient based iterative algorithm can generate more accurate parameter estimates than the stochastic gradient algorithm and the least squares based iterative algorithm has faster convergence rates than the recursive least squares algorithm [32,33]. The proposed methods can be extended to study identification problems of Wiener nonlinear systems [34], Hammerstein-Wiener systems [35], dual-rate sampled-data systems [36][37][38] or other linear or nonlinear systems [39][40][41][42][43].…”

Decomposition based least squares iterative estimation algorithm for two-input single-output output error systems

“…For decades, many identification methods have been presented for state space systems [13,14], linear input-output representation systems [15][16][17], dual-rate sampled-data systems [18][19][20] and nonlinear systems [21][22][23][24]. For example, Ding et al presented the multi-innovation least squares identification methods for pseudo-linear regression systems [25]; Shi et al analyzed a Kalman filter based identification algorithm for systems with randomly missing measurements in a network environment [26]; Zhang et al studied a bias compensation method for stochastic systems with colored noise [27] and for a class of multi-input single-output systems with correlated disturbances using bias compensation methods [28]; Chen et al implemented the algorithms for tuning parameters in regularized least squares problems in system identification [29].…”

Parameter identification of a class of nonlinear systems based on the multi-innovation identification theory

“…For decades, many identification methods have been proposed for linear systems and nonlinear systems, e.g., the instrumental variable methods [5,6], the bias compensation methods [7,8,9], the least squares algorithms [10,11], the multi-innovation identification algorithms [12,13] and the iterative identification methods [14,15,16,17].…”