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Gesture Recognition Based on BP Neural Network Improved by Chaotic Genetic Algorithm
Dong-Jie Li; Yang-Yang Li; Jun-Xiang Li; Yu Fu
发表期刊International Journal of Automation and Computing
ISSN1476-8186
2018
卷号15期号:3页码:267-276
摘要Aim at the defects of easy to fall into the local minimum point and the low convergence speed of back propagation (BP) neural network in the gesture recognition, a new method that combines the chaos algorithm with the genetic algorithm (CGA) is proposed. According to the ergodicity of chaos algorithm and global convergence of genetic algorithm, the basic idea of this paper is to encode the weights and thresholds of BP neural network and obtain a general optimal solution with genetic algorithm, and then the general optimal solution is optimized to the accurate optimal solution by adding chaotic disturbance. The optimal results of the chaotic genetic algorithm are used as the initial weights and thresholds of the BP neural network to recognize the gesture. Simulation and experimental results show that the real-time performance and accuracy of the gesture recognition are greatly improved with CGA.
关键词Gesture recognition back propagation (BP) neural network chaos algorithm genetic algorithm data glove.
DOI10.1007/s11633-017-1107-6
引用统计
被引频次:56[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/42408
专题学术期刊_Machine Intelligence Research
作者单位School of Automation, Harbin University of Science and Technology, Harbin 150080, China
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GB/T 7714
Dong-Jie Li,Yang-Yang Li,Jun-Xiang Li,et al. Gesture Recognition Based on BP Neural Network Improved by Chaotic Genetic Algorithm[J]. International Journal of Automation and Computing,2018,15(3):267-276.
APA Dong-Jie Li,Yang-Yang Li,Jun-Xiang Li,&Yu Fu.(2018).Gesture Recognition Based on BP Neural Network Improved by Chaotic Genetic Algorithm.International Journal of Automation and Computing,15(3),267-276.
MLA Dong-Jie Li,et al."Gesture Recognition Based on BP Neural Network Improved by Chaotic Genetic Algorithm".International Journal of Automation and Computing 15.3(2018):267-276.
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