An Improved Cuckoo Search Optimization Algorithm for the Problem of Chaotic Systems Parameter Estimation

Joint Authors

Wang, Jun
Zhou, Shudao
Zhou, Bihua

Source

Computational Intelligence and Neuroscience

Issue

Vol. 2016, Issue 2016 (31 Dec. 2015), pp.1-8, 8 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2016-01-12

Country of Publication

Egypt

No. of Pages

8

Main Subjects

Biology

Abstract EN

This paper proposes an improved cuckoo search (ICS) algorithm to establish the parameters of chaotic systems.

In order to improve the optimization capability of the basic cuckoo search (CS) algorithm, the orthogonal design and simulated annealing operation are incorporated in the CS algorithm to enhance the exploitation search ability.

Then the proposed algorithm is used to establish parameters of the Lorenz chaotic system and Chen chaotic system under the noiseless and noise condition, respectively.

The numerical results demonstrate that the algorithm can estimate parameters with high accuracy and reliability.

Finally, the results are compared with the CS algorithm, genetic algorithm, and particle swarm optimization algorithm, and the compared results demonstrate the method is energy-efficient and superior.

American Psychological Association (APA)

Wang, Jun& Zhou, Bihua& Zhou, Shudao. 2016. An Improved Cuckoo Search Optimization Algorithm for the Problem of Chaotic Systems Parameter Estimation. Computational Intelligence and Neuroscience،Vol. 2016, no. 2016, pp.1-8.
https://search.emarefa.net/detail/BIM-1099620

Modern Language Association (MLA)

Wang, Jun…[et al.]. An Improved Cuckoo Search Optimization Algorithm for the Problem of Chaotic Systems Parameter Estimation. Computational Intelligence and Neuroscience Vol. 2016, no. 2016 (2015), pp.1-8.
https://search.emarefa.net/detail/BIM-1099620

American Medical Association (AMA)

Wang, Jun& Zhou, Bihua& Zhou, Shudao. An Improved Cuckoo Search Optimization Algorithm for the Problem of Chaotic Systems Parameter Estimation. Computational Intelligence and Neuroscience. 2016. Vol. 2016, no. 2016, pp.1-8.
https://search.emarefa.net/detail/BIM-1099620

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1099620