Suchergebnisse - "multiinnovation stochastic gradient algorithm"

耦合辨识 (3)：部分耦合参数向量信息向量 系统的部分耦合递推参数辨识. (Chinese)

Alternate Title: Coupling Identification. Part C: Partially-Coupled Recursive Parameter Identification for Partially-Coupled Parameter and Information-Vector Systems. (English)

Autoren: 丁 锋 栾小丽 徐 玲 et al.

Quelle: Journal of Qingdao University of Science & Technology (Natural Science Edition); Jun2025, Vol. 46 Issue 3, p1-20, 14p

Parsimonious Model Based Consistent Subspace Identification of Hammerstein Systems Under Periodic Disturbances.

Autoren: Hou, Jie

Quelle: International Journal of Control, Automation & Systems; Jan2024, Vol. 22 Issue 1, p61-71, 11p

Multierror stochastic gradient algorithm for identification of a Hammerstein system with random noise and its application in the modeling of a continuous stirring tank reactor.

Autoren: Jing, Shaoxue

Quelle: Optimal Control - Applications & Methods; May2023, Vol. 44 Issue 3, p1510-1522, 13p

Schlagwörter: SYSTEM identification, UNCERTAINTY (Information theory), PROBABILITY density function, ALGORITHMS, MANUFACTURING processes

An Improved Method for Stochastic Nonlinear System's Identification Using Fuzzy-Type Output-Error Autoregressive Hammerstein–Wiener Model Based on Gradient Algorithm, Multi-Innovation, and Data Filtering Techniques.

Autoren: Ben Halima Abid, Donia Abouda, Saif Eddine Medhaffar, Hanane et al.

Quelle: Complexity; 8/20/2021, p1-29, 29p

Schlagwörter: STOCHASTIC systems, SYSTEM identification, AUTOREGRESSIVE models, NONLINEAR systems, ALGORITHMS

Two-stage Gradient-based Recursive Estimation for Nonlinear Models by Using the Data Filtering.

Autoren: Ji, Yan Kang, Zhen Zhang, Chen

Quelle: International Journal of Control, Automation & Systems; Aug2021, Vol. 19 Issue 8, p2706-2715, 10p

Correlation Analysis-based Stochastic Gradient and Least Squares Identification Methods for Errors-in-variables Systems Using the Multiinnovation.

Autoren: Fan, Shujun Xu, Ling Ding, Feng et al.

Quelle: International Journal of Control, Automation & Systems; 2021, Vol. 19 Issue 1, p289-300, 12p

Multi‐innovation gradient estimation algorithms for multivariate equation‐error autoregressive moving average systems based on the filtering technique.

Autoren: Ma, Ping Ding, Feng Hayat, Tasawar

Quelle: IET Control Theory & Applications (Wiley-Blackwell). Sep2019, Vol. 13 Issue 14, p2086-2094. 9p.

Fault Diagnosis for the Pitch System of Wind Turbines Using the Observer-Based Multi-innovation Stochastic Gradient Algorithm.

Autoren: Wu, Dinghui Liu, Wen Zhai, Yanjie et al.

Quelle: Theory, Methodology, Tools & Applications for Modeling & Simulation of Complex Systems: Part I; 2016, p526-538, 13p

Least-squares-based iterative and gradient-based iterative estimation algorithms for bilinear systems.

Autoren: Li, Meihang Liu, Ximei Ding, Feng

Quelle: Nonlinear Dynamics; Jul2017, Vol. 89 Issue 1, p197-211, 15p

Parameter estimation algorithms for dynamical response signals based on the multi‐innovation theory and the hierarchical principle.

Autoren: Xu, Ling Ding, Feng

Quelle: IET Signal Processing (Wiley-Blackwell); Apr2017, Vol. 11 Issue 2, p228-237, 10p

Hierarchical stochastic gradient identification for non-uniformly sampling hammerstein systems with colored noise.

Autoren: Liu Ranran Zheng Enxing Chang Shan et al.

Quelle: Computer Systems Science & Engineering; Nov2016, Vol. 31 Issue 6, p425-430, 6p

Schlagwörter: STOCHASTIC analysis, PARAMETER identification, HAMMERSTEIN equations, ALGORITHMS, SIMULATION methods & models

A Recursive Least Squares Algorithm for Pseudo-Linear ARMA Systems Using the Auxiliary Model and the Filtering Technique.

Autoren: Guo, Lanjie Wang, Yanjiao Wang, Cheng

Quelle: Circuits, Systems & Signal Processing; Jul2016, Vol. 35 Issue 7, p2655-2667, 13p

Schlagwörter: ELECTRONIC data processing, LEAST squares, ALGORITHMS, NONLINEAR systems, MAXIMUM likelihood statistics

Data filtering-based least squares iterative algorithm for Hammerstein nonlinear systems by using the model decomposition.

Autoren: Ma, Junxia Ding, Feng Yang, Erfu

Quelle: Nonlinear Dynamics; Mar2016, Vol. 83 Issue 4, p1895-1908, 14p

Maximum likelihood based multi-innovation stochastic gradient estimation for controlled autoregressive ARMA systems using the data filtering technique.

Autoren: Feiyan Chen Feng Ding

Quelle: Proceeding of the 11th World Congress on Intelligent Control & Automation; 2014, p5993-5998, 6p

TS-RLS algorithm for pseudo-linear regressive models.

Autoren: Ding, Rui Duan, Honghong

Quelle: 2012 American Control Conference (ACC); 1/ 1/2012, p2683-2688, 6p

Multi-innovation stochastic gradient identification for Hammerstein controlled autoregressive autoregressive systems based on the filtering technique.

Autoren: Mao, Yawen Ding, Feng

Quelle: Nonlinear Dynamics; Feb2015, Vol. 79 Issue 3, p1745-1755, 11p

Several gradient-based iterative estimation algorithms for a class of nonlinear systems using the filtering technique.

Autoren: Wang, Cheng Tang, Tao

Quelle: Nonlinear Dynamics; Aug2014, Vol. 77 Issue 3, p769-780, 12p

Data filtering based least squares algorithms for multivariable CARAR-like systems.

Autoren: Wang, Dong-Qing Ding, Feng Zhu, Da-Qi

Quelle: International Journal of Control, Automation & Systems; Aug2013, Vol. 11 Issue 4, p711-717, 7p

Recursive Relations of the Cost Functions for the Least-Squares Algorithms for Multivariable Systems.

Autoren: Ma, Junxia Ding, Feng

Quelle: Circuits, Systems & Signal Processing; Feb2013, Vol. 32 Issue 1, p83-101, 19p

Schlagwörter: RECURSIVE functions, LEAST squares, ALGORITHMS, MULTIVARIATE analysis, LINEAR statistical models, ERROR analysis in mathematics, COMPUTER simulation, COST functions, PARAMETER estimation

Multi-Innovation Stochastic Gradient Identification Algorithm for Hammerstein Controlled Autoregressive Autoregressive Systems Based on the Key Term Separation Principle and on the Model Decomposition.

Autoren: Huiyi Hu Xiao Yongsong Rui Ding

Quelle: Journal of Applied Mathematics; 2013, p1-7, 7p

Schlagwörter: STOCHASTIC analysis, ALGORITHMS, HAMMERSTEIN equations, AUTOREGRESSIVE models, MATHEMATICAL decomposition, MATHEMATICAL models, NONLINEAR analysis