摘要
该文研究了量子理论与量子神经网络原理,深入分析了量子前向对传网模型与基于递归加权最小二乘的量子前向对传算法。提出了量子前向对传网的定义与知识集,提出了自适应量子前向对传算法,证明了算法的收敛性。该算法全面考虑了在本次学习之前学习速率的总体状况,通过自适应地改变学习速率,控制学习速率适时变化,改善网络的收敛性。有效克服了学习速率过高导致网络振荡发散与学习速率太小降低网络收敛速度的缺陷。仿真结果表明,自适应量子前向对传算法相对基于递归加权最小二乘的量子前向对传算法具有较少的网络训练迭代次数和较高的分类精度。
This paper studies the quantum theory and the principle of Quantum Neural Network （QNN）. Model of Quantum Forward Counter Propagation Neural Network （QFCPNN） and Recursive Weighted Least Squares Quantum Forward Counter Propagation Algorithm （RWLS_QFCPA） are analyzed. Definition and knowledge set of QFCPNN is proposed. Adaptive Quantum Forward Counter Propagation Algorithm （AQFCPA） is proposed and its convergence is proved. Full account of overall situations of learning rates before current learning, this algorithm improves network convergence by adaptively changing the learning rate and controls timely changing learning rate. This new algorithm effectively overcomes some defects including network oscillations divergence due to high learning rate and reducing network convergence speed due to low learning rate. The simulation results indicate that AQFCPA has less number of iterations of network training and higher classification accuracy relative to RWLS_QFCPA.
出处
《电子与信息学报》
EI
CSCD
北大核心
2013年第11期2778-2783,共6页
Journal of Electronics & Information Technology
基金
陕西省科技厅自然科学基础研究计划(2011JQ9004)资助课题
关键词
量子神经网络
量子前向对传网
自适应
收敛性
Quantum Neural Network （QNN）
Quantum Forward Counter Propagation Neural Network （QFCPNN）
Adaptive
Convergence
