DocumentCode
3529342
Title
Improved genetic neural network for image segmentation
Author
Wang, Qing-sheng ; Zhang, Yue-qin ; Hu, Bin ; Zhao, Jian-guang
Author_Institution
Comput. Sci. & Technol. Inst., Taiyuan Univ. of Technol., Taiyuan, China
Volume
Part 3
fYear
2011
fDate
3-5 Sept. 2011
Firstpage
1694
Lastpage
1698
Abstract
The paper provides the method of the improved genetic neural network for image segmentation. The method uses improved genetic algorithm BP neural network weights and thresholds to optimize, and use the definition of bipolar fitness function mapping compression to speed up neural network training speed, and then use iterative improved neural network algorithm to achieve image segmentation. The results of experimental show that the improved genetic neural network can better achieve the image segmentation, compared with the traditional method; Compared with BP neural network training speed is greatly improved.
Keywords
backpropagation; genetic algorithms; image segmentation; iterative methods; neural nets; bipolar fitness function mapping compression; image segmentation; improved genetic algorithm BP neural network; iterative improved neural network algorithm; Arrays; Genetic algorithms; Genetics; Image segmentation; Simulation; Support vector machine classification; Training; Genetic neural Network; Image segmentation;
fLanguage
English
Publisher
ieee
Conference_Titel
Industrial Engineering and Engineering Management (IE&EM), 2011 IEEE 18Th International Conference on
Conference_Location
Changchun
Print_ISBN
978-1-61284-446-6
Type
conf
DOI
10.1109/ICIEEM.2011.6035488
Filename
6035488
Link To Document