基于量子粒子群优化反向传播神经网络的手势识别

Gesture recognition based on quantum-behaved particle swarm optimization of back propagation neural network

反向传播(BP)神经网络算法在手势识别中得到了广泛的应用。为了对算法进行改进以提高BP神经网络的学习效率,提出一种基于量子粒子群优化BP神经网络的手势识别训练算法。在手势识别过程中,首先采用量子粒子群算法(QPSO)训练BP神经网络,获得优化的BP神经网络权值和阈值;合理地定义并提取BP神经网络的手势识别样本;最后采用训练过的BP神经网络对动态手势进行识别。该算法简单,不依赖初始值,并且收敛速度快,尤其对于高维复杂问题,能保证收敛到最优解。实验结果表明,该算法平均训练时间达到5.15 s,识别正确率达到95.1%,效果明显优于一般的BP神经网络算法。

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.