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The Scientific World Journal - Volume 2014 2014, Article ID 878262, 8 pages -

Research ArticleSchool of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, Hunan 410014, China

Received 1 January 2014; Accepted 16 February 2014; Published 20 March 2014

Academic Editors: S.-F. Chien, T. O. Ting, and X.-S. Yang

Copyright © 2014 Jiao-hong Yi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.

Autor: Jiao-hong Yi, Wei-hong Xu, and Yuan-tao Chen



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