【作者】 孟祥萍； 魏本艳； 于雪芳； 苑全德； 皮玉珍；

【Author】 Meng Xiang-ping~1 Wei Ben-yan~2 Yu Xue-fang~3 Yuan Quan-de~1 Pi Yu-zhen~1 (1.Changchun Institute of Technology,Department of Electrical Engineering,Jilin,Changchun,130012) (2.Northeast Dianli University,Information engineering institute,Jilin,132012;3.Northeast Dianli University,Electrical engineering institute,Jilin,132012)

【机构】 长春工程学院电信学院； 东北电力大学信息工程学院； 东北电力大学电气工程学院；

【摘要】 人工神经网络是可用于建模、探讨各种复杂非线性现象的强大工具。本文提出了一种新的基于多Agent系统理论(MAS)和量子算法的人工神经网络。在该人工神经网络中所有节点都为有学习能力的量子计算Agent(QCMAS)。通过训练QCMAS强化学习,提出了新的人工神经训练方法。新的人工神经具有强大的并行工作能力而且它的训练时间比经典算法短,实验结果证明了该方法的有效性。更多还原

【Abstract】 Artificial Neural Networks are powerful tools that can be used to model and investigate various complex and non-linear phenomena.In this study,we construct a new ANN,which is based on Multi-Agent System(MAS) theory and quantum computing algorithm.All nodes in this new ANN are presented as Quantum Computational(QC) agents,and these agents have learning ability.A novel ANN training method was proposed via implementing QCMAS reinforcement learning.This new ANN has powerful parallel-work ability and its training time is shorter than classic algorithm.Experiment results show that this method is effective.更多还原