Radar Target Classification Using an Evolutionary Extreme Learning Machine Based on Improved Quantum-Behaved Particle Swarm Optimization
Joint Authors
Zhao, Feixiang
Liu, Yongxiang
Huo, Kai
Zhang, Zhongshuai
Source
Mathematical Problems in Engineering
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-12-06
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
A novel evolutionary extreme learning machine (ELM) based on improved quantum-behaved particle swarm optimization (IQPSO) for radar target classification is presented in this paper.
Quantum-behaved particle swarm optimization (QPSO) has been used in ELM to solve the problem that ELM needs more hidden nodes than conventional tuning-based learning algorithms due to the random set of input weights and hidden biases.
But the method for calculating the characteristic length of Delta potential well of QPSO may reduce the global search ability of the algorithm.
To solve this issue, a new method to calculate the characteristic length of Delta potential well is proposed in this paper.
Experimental results based on the benchmark functions validate the better performance of IQPSO against QPSO in most cases.
The novel algorithm is also evaluated by using real-world datasets and radar data; the experimental results indicate that the proposed algorithm is more effective than BP, SVM, ELM, QPSO-ELM, and so on, in terms of real-time performance and accuracy.
American Psychological Association (APA)
Zhao, Feixiang& Liu, Yongxiang& Huo, Kai& Zhang, Zhongshuai. 2017. Radar Target Classification Using an Evolutionary Extreme Learning Machine Based on Improved Quantum-Behaved Particle Swarm Optimization. Mathematical Problems in Engineering،Vol. 2017, no. 2017, pp.1-13.
https://search.emarefa.net/detail/BIM-1191725
Modern Language Association (MLA)
Zhao, Feixiang…[et al.]. Radar Target Classification Using an Evolutionary Extreme Learning Machine Based on Improved Quantum-Behaved Particle Swarm Optimization. Mathematical Problems in Engineering No. 2017 (2017), pp.1-13.
https://search.emarefa.net/detail/BIM-1191725
American Medical Association (AMA)
Zhao, Feixiang& Liu, Yongxiang& Huo, Kai& Zhang, Zhongshuai. Radar Target Classification Using an Evolutionary Extreme Learning Machine Based on Improved Quantum-Behaved Particle Swarm Optimization. Mathematical Problems in Engineering. 2017. Vol. 2017, no. 2017, pp.1-13.
https://search.emarefa.net/detail/BIM-1191725
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1191725