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Gesture Recognition Based on BP Neural Network Improved by Chaotic Genetic Algorithm

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Abstract

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 realtime performance and accuracy of the gesture recognition are greatly improved with CGA.

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Authors and Affiliations

  1. School of Automation, Harbin University of Science and Technology, Harbin, 150080, China

    Dong-Jie Li, Yang-Yang Li, Jun-Xiang Li & Yu Fu

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  1. Dong-Jie Li
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Corresponding author

Correspondence to Dong-Jie Li.

Additional information

This work was supported by Natural Science Foundation of Heilongjiang Province Youth Fund (No. QC2014C054), Foundation for University Young Key Scholar by Heilongjiang Province (No. 1254G023) and the Science Funds for the Young Innovative Talents of HUST (No. 201304).

Recommended by Associate Editor Victor Becerra

Dong-Jie Li received the B. Sc. degree in mechanical design manufacturing and automation, Harbin University of Science and Technology, China in 2004, the M. Sc. degree in mechatronic engineering from Harbin University of Science and Technology, China in 2007, and the Ph. D. degree in mechanical manufacture and automation from Harbin University of Science and Technology, China in 2009. From 2010 to 2013, she was a postdoctoral researcher at Harbin Institute of Technology. She visited the Georgia Institute of Technology, USA from March 2016 to March 2017. She is currently a professor of Harbin University of Science and Technology. She is the author and coauthor of more than 50 publications.

Her research interests include micro/nano manipulation, application of intelligent control, and intelligent mechatronic system.

Yang-Yang Li received the B. Sc. degree in automation, Harbin University of Science and Technology, China in 2015. His now a postgraduate in control theory and control engineering, Harbin University of Science and Technology, China.

His research interest is robot intelligent control.

Jun-Xiang Li received the B. Sc. degree in automation, Harbin University of Science and Technology, China in 2013, the M. Sc. degree in pattern recognition and intelligent system from Harbin University of Science and Technology, China in 2016. He is now working for Delta Electronics Co. Ltd.

His research interests include pattern recognition and intelligent control system.

Yu Fu received the B. Sc. degree in automation, Harbin University of Science and Technology, China in 2015. She is now a postgraduate in control engineering, Harbin University of Science and Technology, China.

Her research interest is application of intelligent control.

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Li, DJ., Li, YY., Li, JX. et al. Gesture Recognition Based on BP Neural Network Improved by Chaotic Genetic Algorithm. Int. J. Autom. Comput. 15, 267–276 (2018). https://doi.org/10.1007/s11633-017-1107-6

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  • DOI: https://doi.org/10.1007/s11633-017-1107-6

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