摘要：
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。
关键词:
受控量子旋转门,
量子神经元,
量子神经网络,
比例积分微分参数调整,
量子比特相位
Abstract:
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.
Key words:
controlled quantum rotation gate,
quantum neuron,
Quantum Neural Networks(QNNs),
Proportion Integration Differentiation(PID) parameters adjustment,
phase of qubits
中图分类号:
曹茂俊, 李盼池, 肖红. 量子神经网络在PID参数调整中的应用[J]. 计算机工程, 2011, 37(12): 182-184.
CAO Mao-Dun, LI Fen-Che, XIAO Gong. Application of Quantum Neural Networks in Proportion Integration Differentiation Parameters Adjustment[J]. Computer Engineering, 2011, 37(12): 182-184.