Separable multi‐innovation stochastic gradient estimation algorithm for the nonlinear dynamic responses of systems-Reference-Cited by-同舟云学术

Separable multi‐innovation stochastic gradient estimation algorithm for the nonlinear dynamic responses of systems

Published:2020-04-21 Issue:7 Volume:34 Page:937-954
ISSN:0890-6327
Container-title:International Journal of Adaptive Control and Signal Processing
language:en
Short-container-title:Int J Adapt Control Signal Process

Author:

Xu Ling¹^ORCID,Ding Feng¹^ORCID,Wan Lijuan²,Sheng Jie³

Affiliation:

1. Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things EngineeringJiangnan University Wuxi China

2. College of Automation and Electronic EngineeringQingdao University of Science and Technology Qingdao China

3. School of Engineering and TechnologyUniversity of Washington Tacoma Washington USA

Publisher

Wiley

Subject

Electrical and Electronic Engineering,Signal Processing,Control and Systems Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/acs.3113

Reference79 articles.

1. The least squares based iterative algorithms for parameter estimation of a bilinear system with autoregressive noise using the data filtering technique

2. Two-stage Gradient-based Iterative Estimation Methods for Controlled Autoregressive Systems Using the Measurement Data

3. Auxiliary Model Based Least Squares Iterative Algorithms for Parameter Estimation of Bilinear Systems Using Interval-Varying Measurements

4. Maximum Likelihood Recursive Identification for the Multivariate Equation-Error Autoregressive Moving Average Systems Using the Data Filtering

5. Particle filtering based parameter estimation for systems with output-error type model structures

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