Gesture recognition based on quantum-behaved particle swarm optimization of back propagation neural network
Yang Zhiq · Journal of Computer Applications · 2014
Back Propagation( BP) neural network algorithm is widely used in hand gesture recognition. In order to improve learning efficiency of the BP neural network, the authors proposed a hand gesture recognition algorithm based on Quantum-behaved Particle Swarm Optimization( QPSO) of BP neural network. In the process of gesture recognition, first, the QPSO algorithm was used to train the BP neural network and get the weights and thresholds of the optimized BP neural network. Experiment program defined and extracted gesture recognition samples reasonably for the BP neural network. Finally,the dynamic gestures were recognized by the trained BP neural network. The proposed algorithm is simple, does not depend on the initial value, and has a fast convergence speed, especially for high dimensional complex problems, it can guarantee the convergence to the optimal solution. The experimental results indicate that the average training time of the new algorithm can reach 5. 15 seconds, the correct recognition rate of the new algorithm can reach 95. 1%. The new algorithm has better effects than the general BP neural network algorithm.