Journal ArticleDOI
A recursive least squares parameter estimation algorithm for output nonlinear autoregressive systems using the input–output data filtering
TLDR
A data filtering based recursive least squares algorithm is proposed based on the data filtering technique and results show that the proposed algorithm can generate more accurate parameter estimates than the recursive generalized most squares algorithm.Abstract:
Nonlinear systems exist widely in industrial processes. This paper studies the parameter estimation methods of establishing the mathematical models for a class of output nonlinear systems, whose output is nonlinear about the past outputs and linear about the inputs. We use an estimated noise transfer function to filter the input–output data and obtain two identification models, one containing the parameters of the system model, and the other containing the parameters of the noise model. Based on the data filtering technique, a data filtering based recursive least squares algorithm is proposed. The simulation results show that the proposed algorithm can generate more accurate parameter estimates than the recursive generalized least squares algorithm.read more
Citations
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Journal ArticleDOI
Hierarchical Parameter Estimation for the Frequency Response Based on the Dynamical Window Data
TL;DR: In this paper, a hierarchical multi-innovation stochastic gradient estimation method is derived through parameter decomposition, and the forgetting factor and the convergence factor are introduced to improve the performance of the algorithm.
Journal ArticleDOI
The least squares based iterative algorithms for parameter estimation of a bilinear system with autoregressive noise using the data filtering technique
Meihang Li,Ximei Liu +1 more
TL;DR: A two-stage least squares based iterative algorithm and a filtering based least squares iterative algorithms are proposed for estimating the parameters of bilinear systems with colored noises by using the hierarchical identification principle and the data filtering technique.
Journal ArticleDOI
A hierarchical least squares identification algorithm for Hammerstein nonlinear systems using the key term separation
TL;DR: This paper considers the parameter identification for Hammerstein controlled autoregressive systems by using the key term separation technique to express the system output as a linear combination of the system parameters, and then a hierarchical least squares algorithm is developed for estimating all parameters involving in the subsystems.
Journal ArticleDOI
State filtering-based least squares parameter estimation for bilinear systems using the hierarchical identification principle
TL;DR: This study presents a combined parameter and state estimation algorithm for a bilinear system described by its observer canonical state-space model based on the hierarchical identification principle to reduce the computation burden and improve the parameter tracking capability.
Journal ArticleDOI
Combined state and parameter estimation for a bilinear state space system with moving average noise
TL;DR: An interactive estimation algorithm for unmeasurable states and parameters based on the hierarchical identification principle for bilinear systems with measurement noise in the form of the moving average model is presented.
References
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Book
System Identification: Theory for the User
TL;DR: Das Buch behandelt die Systemidentifizierung in dem theoretischen Bereich, der direkte Auswirkungen auf Verstaendnis and praktische Anwendung der verschiedenen Verfahren zur IdentifIZierung hat.
Journal ArticleDOI
Intelligent Particle Filter and Its Application to Fault Detection of Nonlinear System
Shen Yin,Xiangping Zhu +1 more
TL;DR: In this paper, a modified particle filter, i.e., intelligent particle filter (IPF), is proposed, inspired from the genetic algorithm, which mitigates particle impoverishment and provides more accurate state estimation results compared with the general PF.
Journal ArticleDOI
Co-evolutionary particle swarm optimization algorithm for two-sided robotic assembly line balancing problem
TL;DR: Computational and statistical results demonstrate that the proposed co-evolutionary particle swarm optimization outperforms most of the other metaheuristics for majority of the problems considered in the study.
Journal ArticleDOI
Identification of Hammerstein-Wiener models
TL;DR: A new maximum-likelihood based method for the identification of Hammerstein-Wiener model structures is developed and illustrated that addresses the blind Wiener estimation problem as a special case.
Journal ArticleDOI
A filtering based multi-innovation extended stochastic gradient algorithm for multivariable control systems
TL;DR: A filtering based extended stochastic gradient algorithm and a filtering based multi-innovation ESG algorithm for improving the parameter estimation accuracy for a multivariable system with moving average noise.