The Hierarchical Iterative Identification Algorithm for Multi-Input-Output-Error Systems with Autoregressive Noise

This paper considers the identification problem of multi-input-output-error autoregressive systems. A hierarchical gradient based iterative (H-GI) algorithm and a hierarchical least squares based iterative (H-LSI) algorithm are presented by using the hierarchical identification principle. A gradient based iterative (GI) algorithm and a least squares based iterative (LSI) algorithm are presented for comparison. The simulation results indicate that the H-LSI algorithm can obtain more accurate parameter estimates than the LSI algorithm, and the H-GI algorithm converges faster than the GI algorithm.

[1]  Huiping Li,et al.  On Neighbor Information Utilization in Distributed Receding Horizon Control for Consensus-Seeking , 2016, IEEE Transactions on Cybernetics.

[2]  Feng Ding,et al.  Novel data filtering based parameter identification for multiple-input multiple-output systems using the auxiliary model , 2016, Autom..

[3]  Maryam Dehghani,et al.  Identification of multivariable nonlinear systems in the presence of colored noises using iterative hierarchical least squares algorithm. , 2014, ISA transactions.

[4]  Min-Sen Chiu,et al.  Correlation analysis based MIMO neuro-fuzzy Hammerstein model with noises , 2016 .

[5]  Qingxia Li,et al.  Array Factor Forming for Image Reconstruction of One-Dimensional Nonuniform Aperture Synthesis Radiometers , 2016, IEEE Geoscience and Remote Sensing Letters.

[6]  Dongqing Wang,et al.  Hierarchical parameter estimation for a class of MIMO Hammerstein systems based on the reframed models , 2016, Appl. Math. Lett..

[7]  Ling Xu,et al.  Application of the Newton iteration algorithm to the parameter estimation for dynamical systems , 2015, J. Comput. Appl. Math..

[8]  Guido Herrmann,et al.  Robust adaptive finite‐time parameter estimation and control for robotic systems , 2015 .

[9]  Jing Lu,et al.  Least squares based iterative identification for a class of multirate systems , 2010, Autom..

[10]  Jianqiang Pan,et al.  A filtering based multi-innovation extended stochastic gradient algorithm for multivariable control systems , 2017 .

[11]  Wan Xiangkui,et al.  A T-wave alternans assessment method based on least squares curve fitting technique , 2016 .

[12]  Feng Ding,et al.  Decomposition based least squares iterative identification algorithm for multivariate pseudo-linear ARMA systems using the data filtering , 2017, J. Frankl. Inst..

[13]  Nan Zhao,et al.  Android-based mobile educational platform for speech signal processing , 2017 .

[14]  Feng Ding,et al.  Iterative identification algorithms for input nonlinear output error autoregressive systems , 2016 .

[15]  Jian Pan,et al.  Image noise smoothing using a modified Kalman filter , 2016, Neurocomputing.

[16]  Feng Ding,et al.  Recursive least squares algorithm and gradient algorithm for Hammerstein–Wiener systems using the data filtering , 2016 .

[17]  Andrea Cristofaro,et al.  Fault accommodation for multi‐input linear sampled‐data systems , 2015 .

[18]  Feng Ding,et al.  The maximum likelihood least squares based iterative estimation algorithm for bilinear systems with autoregressive moving average noise , 2017, J. Frankl. Inst..

[19]  Dandan Meng.  Recursive Least Squares and Multi-innovation Gradient Estimation Algorithms for Bilinear Stochastic Systems , 2017, Circuits Syst. Signal Process..

[20]  Dingli Yu,et al.  PID controller tuning for a multivariable glass furnace process by genetic algorithm , 2016, International Journal of Automation and Computing.

[21]  Feng Ding,et al.  Some new results of designing an IIR filter with colored noise for signal processing , 2018, Digit. Signal Process..

[22]  Feng Ding,et al.  The Gradient-Based Iterative Estimation Algorithms for Bilinear Systems with Autoregressive Noise , 2017, Circuits, Systems, and Signal Processing.

[23]  Feng Ding,et al.  Multiperiodicity and Exponential Attractivity of Neural Networks with Mixed Delays , 2017, Circuits Syst. Signal Process..

[24]  F. Ding,et al.  Least-squares-based iterative and gradient-based iterative estimation algorithms for bilinear systems , 2017 .

[25]  T. Hayat,et al.  Parameter estimation for pseudo-linear systems using the auxiliary model and the decomposition technique , 2017 .

[26]  Rames C. Panda,et al.  Parameter estimation of linear MIMO systems using sequential relay feedback test , 2014 .

[27]  Yu Guo,et al.  Robust adaptive estimation of nonlinear system with time‐varying parameters , 2015 .

[28]  Zhen Zhang,et al.  Maximum likelihood estimation method for dual-rate Hammerstein systems , 2017 .

[29]  Yan Ji,et al.  Unified Synchronization Criteria for Hybrid Switching-Impulsive Dynamical Networks , 2015, Circuits Syst. Signal Process..

[30]  Knut Möller,et al.  Hierarchical Parameter Identification in Models of Respiratory Mechanics , 2011, IEEE Transactions on Biomedical Engineering.

[31]  Ling Xu,et al.  Parameter estimation and controller design for dynamic systems from the step responses based on the Newton iteration , 2015 .

[32]  Ling Xu,et al.  A proportional differential control method for a time-delay system using the Taylor expansion approximation , 2014, Appl. Math. Comput..

[33]  Hui Zhang,et al.  Synchronization of uncertain chaotic systems via complete-adaptive-impulsive controls , 2017 .

[34]  Laurent Bako,et al.  Parameterization and identification of multivariable state-space systems: A canonical approach , 2011, Autom..

[35]  David Zumoffen,et al.  Improvements on multivariable control strategies tested on the Petlyuk distillation column , 2013 .

[36]  Feng Ding,et al.  Parameter estimation algorithms for dynamical response signals based on the multi-innovation theory and the hierarchical principle , 2017, IET Signal Process..

[37]  Guoxiang Gu,et al.  Networked stabilization for multi-input systems over quantized fading channels , 2015, Autom..

[38]  Feng Ding,et al.  Highly Efficient Identification Methods for Dual-Rate Hammerstein Systems , 2015, IEEE Transactions on Control Systems Technology.

[39]  Yu Guo,et al.  Robust adaptive parameter estimation of sinusoidal signals , 2015, Autom..

[40]  Rui Liu,et al.  Monitoring strategy for relay incentive mechanism in cooperative communication networks , 2017, Comput. Electr. Eng..

[41]  F. Ding,et al.  Performance analysis of the generalised projection identification for time-varying systems , 2016 .

[42]  Feng Ding,et al.  Recursive Least Squares and Multi-innovation Stochastic Gradient Parameter Estimation Methods for Signal Modeling , 2017, Circuits Syst. Signal Process..

[43]  Feng Ding,et al.  A multi-innovation state and parameter estimation algorithm for a state space system with d-step state-delay , 2017, Signal Process..

[44]  Ling Xu,et al.  The damping iterative parameter identification method for dynamical systems based on the sine signal measurement , 2016, Signal Process..

[45]  Feng Ding,et al.  Parameter estimation algorithms for multivariable Hammerstein CARMA systems , 2016, Inf. Sci..

[46]  Feng Ding,et al.  Hierarchical gradient based and hierarchical least squares based iterative parameter identification for CARARMA systems , 2014, Signal Process..

[47]  Feng Ding,et al.  Joint state and multi-innovation parameter estimation for time-delay linear systems and its convergence based on the Kalman filtering , 2017, Digit. Signal Process..

[48]  F. Ding,et al.  Recasted models-based hierarchical extended stochastic gradient method for MIMO nonlinear systems , 2017 .

[49]  Michel Verhaegen,et al.  Blind multivariable ARMA subspace identification , 2016, Autom..

[50]  Huai‐Ning Wu,et al.  Observer design and output feedback stabilization for nonlinear multivariable systems with diffusion PDE-governed sensor dynamics , 2013 .

[51]  Feng Ding,et al.  Recursive parameter and state estimation for an input nonlinear state space system using the hierarchical identification principle , 2015, Signal Process..

[52]  Jiling Ding,et al.  Recursive and Iterative Least Squares Parameter Estimation Algorithms for Multiple-Input–Output-Error Systems with Autoregressive Noise , 2018, Circuits Syst. Signal Process..