量子神经网络在PID参数调整中的应用

Application of Quantum Neural Networks in Proportion Integration Differentiation Parameters Adjustment

提出一种基于量子神经网络(QNNs)的比例积分微分(PID)参数在线调整方法。通过构造受控量子旋转门,给出一个量子神经元模型,其中包括输入量子比特相位的旋转角度和控制量2种设计参数。在此基础上提出一个量子神经网络模型,利用梯度下降法设计该模型的学习算法,并将其用于PID参数的在线调整,实验结果表明,QNNs的调整能力及稳定性均优于反向传播网络。

This paper presents an online adjusting method of Proportion Integration Differentiation（PID） parameters based on Quantum Neural Networks（QNNs）. By designing a controlled quantum rotation gate, a quantum neuron model is constructed, including two kinds of design parameters： rotation angle of qubits phase and its control range. A quantum neural networks model based on quantum neuron is proposed. By using gradient descent algorithm, a learning algorithm of the model is designed. Experimental results show that both the adjusting ability and the stability of QNNs model are superior to that of the Back Propagation（BP） networks.