基于量子卷积神经网络算法的微小零件识别

Recognition of micro parts based on quantum convolution neural network algorithm

设计了量子卷积神经网络表示层、隐藏层神经元和输出层神经元模型;采用修正线性激活函数ReLu作为激活函数,并通过训练误差函数优化量子旋转角度和神经连接权值。8种微小零件的仿真试验表明,量子卷积神经网络算法的识别准确率较高,耗时少且识别效果较好。

The representation layer,hidden layer and output layer neuron models of quantum convolution neural network were designed.Modified linear activation function Relu was used as the activation function,and the quantum rotation angle and neural connection weight were optimized by training error function.The simulation results of eight kinds of micro parts show that the recognition accuracy of quantum convolution neural network algorithm is higher,the time consumption is less and the recognition effect is better than other algorithms.