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Novel Back Propagation Optimization by Cuckoo Search Algorithm
Joint Authors
Yi, Jiao-hong
Chen, Yuantao
Xu, Weihong
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-03-20
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
The traditional Back Propagation (BP) has some significant disadvantages, such as training too slowly, easiness to fall into local minima, and sensitivity of the initial weights and bias.
In order to overcome these shortcomings, an improved BP network that is optimized by Cuckoo Search (CS), called CSBP, is proposed in this paper.
In CSBP, CS is used to simultaneously optimize the initial weights and bias of BP network.
Wine data is adopted to study the prediction performance of CSBP, and the proposed method is compared with the basic BP and the General Regression Neural Network (GRNN).
Moreover, the parameter study of CSBP is conducted in order to make the CSBP implement in the best way.
American Psychological Association (APA)
Yi, Jiao-hong& Xu, Weihong& Chen, Yuantao. 2014. Novel Back Propagation Optimization by Cuckoo Search Algorithm. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-1051458
Modern Language Association (MLA)
Yi, Jiao-hong…[et al.]. Novel Back Propagation Optimization by Cuckoo Search Algorithm. The Scientific World Journal No. 2014 (2014), pp.1-8.
https://search.emarefa.net/detail/BIM-1051458
American Medical Association (AMA)
Yi, Jiao-hong& Xu, Weihong& Chen, Yuantao. Novel Back Propagation Optimization by Cuckoo Search Algorithm. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-1051458
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1051458