Novel Hybrid Optimization Algorithm for Parameter Estimation of Chaotic System

DOI

10.2991/ifmca-16.2017.44How to use a DOI?

Keywords

chaotic system, parameter estimation, hybrid optimization algorithm

Abstract

This paper proposes a novel hybrid optimization algorithm of Adaptive Cuckoo Search and Particle Swarm Optimization algorithm for parameter estimation of chaotic system. In order to enhance the accuracy and efficiency of ACS, the strategy of exploitation velocity adjustment via acceleration by distance of PSO algorithm is adopted. Thus, the algorithms mentioned above are used for estimation of the parameters of Lorenz chaotic system. Estimation result from each algorithm generates a standard deviation with the true parameter data, which is regarded as the fitness. Compared with ACS and PSO algorithm, the hybrid optimization algorithm is more efficient and accurate for parameter estimation, thus benefitting the simulation and control of chaotic systems.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Haotian Chang
AU  - Jing Feng
AU  - Lei Jiang
PY  - 2017/03
DA  - 2017/03
TI  - Novel Hybrid Optimization Algorithm for Parameter Estimation of Chaotic System
BT  - Proceedings of the 2016 International Forum on Mechanical, Control and Automation (IFMCA 2016)
PB  - Atlantis Press
SP  - 277
EP  - 282
SN  - 2352-5401
UR  - https://doi.org/10.2991/ifmca-16.2017.44
DO  - 10.2991/ifmca-16.2017.44
ID  - Chang2017/03
ER  -