基于GA?BP神经网络的手势识别精度优化研究

郭兴 徐武 唐文权

摘  要： 针对5DT数据手套手势识别过程中存在的精度问题，传统的BP神经网络算法受到其自身因素的影响，导致出现输出手势缺失、变形、精度差的问题。为此，该文提出一种GA?BP权值优化算法，能有效克服BP算法局部寻优的缺点，使输出值不断地接近期望数值，防止陷入局部极小的情况，可以克服输出图像缺失、变形的问题。在GA?BP算法的基础上，对函数输出误差的最大值进行权值优化，解决输出手势精度差的问题。实验结果表明，基于GA?BP神经网络权值优化算法改善了手势识别的精度。

关键词： 手势识别; 精度优化; GA?BP神经网络; 权值优化; 效果分析; 算法仿真验证

中图分类号： TN915.06?34; TP391.9             文献标识码： A                      文章编号： 1004?373X（2020）06?0183?04

Research on gesture recognition accuracy optimization based on GA?BP neural network

GUO Xing， XU Wu， TANG Wenquan

（College of Electrical and Information Engineering， Yunnan Minzu University， Kunming 650500， China）

Abstract： For the accuracy problem in the process of gesture recognition of 5DT data gloves， the traditional BP neural network algorithm is affected by its own factors， resulting in the absence， deformation and poor precision of the output gesture. A GA?BP weight optimization algorithm is proposed， which can effectively overcome the shortcomings of local optimization of BP algorithm， make the output value close to the expected value continuously， prevent getting into the local minimum， and overcome the missing and distortion of output image. On the basis of the GA?BP algorithm， the weight optimization for the maximum value of the function output error is conducted to improve the precision of the output gesture. The experimental results show that the weight optimization algorithm based on GA?BP neural network can improve the accuracy of gesture recognition.

Keywords： gesture recognition; precision optimization; GA?BP neural network; weight optimization; image output; effect analysis; algorithm simulation verification