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

• Published in: International journal of automation and computing Vol. 15; no. 3; pp. 267 - 276
• Main Authors: Li, Dong-Jie, Li, Yang-Yang, Li, Jun-Xiang, Fu, Yu
• Format: Journal Article
• Language: English
• Published: Beijing Institute of Automation, Chinese Academy of Sciences 01.06.2018; Springer Nature B.V
• Subjects: Accuracy; Automation; Back propagation; Back propagation networks; Basic converters; CAE) and Design; Computer Applications; Computer-Aided Engineering (CAD; Control; Convergence; Engineering; Genetic algorithms; Gesture recognition; Hierarchies; Learning; Mechatronics; Mutation; Neural networks; Optimization; Propagation; Research Article; Robotics; Sensors; Thresholds; genetic algorithm; chaos algorithm; back propagation (BP) neural network; Gesture recognition; data glove
• ISSN: 1476-8186; 2153-182X; 1751-8520; 2153-1838
• DOI: 10.1007/s11633-017-1107-6

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.