.png){kind=icon}
Return
Decomposition based least squares iterative estimation algorithm for two-input single-output output error systems
.png){kind=icon}
PRE
AI
DOI：10.1016/j.jfranklin.2014.10.001.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper develops a decomposition based least squares iterative identification algorithm for two-input single-output (TISO) systems. The basic idea is to decompose a TISO system into two subsystems and then to identify each subsystem, respectively. Compared with the least squares based iterative algorithm, the proposed algorithm has less computational load. The simulation results indicate that the proposed algorithm is effective. (C) 2014 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
Keywords:
MOVING AVERAGE SYSTEMS
MODEL-PREDICTIVE CONTROL
TIME NONLINEAR-SYSTEMS
PARAMETER-ESTIMATION
MULTIVARIABLE SYSTEMS
MATRIX EQUATIONS
COLORED NOISE
IDENTIFICATION
STATE
DELAYS
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
J
IF:
3.7
Papers:
6.4K
Citations:
1.5W
Organization
Cited Papers
ShareSaveShareSaveModeling and parameter identification of systems with multisegment piecewise-linear characteristics
PREAI
ShareSaveShareSaveShareSaveP-index theory for linear Hamiltonian systems and multiple solutions for nonlinear Hamiltonian systems.png)
Nonlinearity
NonlinearityIF0
PREAI
ShareSaveShareSave
View more