Maximum likelihood parameter estimation of dynamic systems by heuristic swarm search-Reference-Cited by-同舟云学术

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Maximum likelihood parameter estimation of dynamic systems by heuristic swarm search

Published:2017-01-10 Issue:1 Volume:21 Page:97-116
ISSN:1088-467X
Container-title:Intelligent Data Analysis
language:
Short-container-title:IDA

Author:

Lu Yongzhong¹,Yan Danping²,Zhou Min²,Levy David³

Affiliation:

1. School of Software Engineering, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China

2. College of Public Administration, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China

3. Faculty of Engineering and Information Technologies, University of Sydney, Sydney 2006, Australia

Publisher

IOS Press

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Theoretical Computer Science

Reference44 articles.

1. Haryanto A. and Hong K.-S., Maximum likelihood identification of Wiener-Hammerstein models, Mechanical Systems and Signal Processing 41(1-2) (2013), 54-70.

2. Recursive maximum likelihood parameter estimation for state space systems using polynomial chaos theory;Pence;Automatica,2011

3. A distance-based locally informed particle swarm model for multimodal optimization;Qu;IEEE Transactions on Evolutionary Computation,2013

4. Hu C. and Liu Q., Online identification for hypersonic vehicle using recursive maximum likelihood method based on interior-point algorithm, in: 25th Chinese Control and Decision Conference, IEEE, (2013), 1862-1867.

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1. Data-driven physical law learning model for chaotic robot dynamics prediction;Applied Intelligence;2022-01-21

2. Fractional order chaotic cryptography in colored noise environment by using fractional order interpolatory cubature Kalman filter;Transactions of the Institute of Measurement and Control;2019-02-04

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