摘要
在量子理论的框架内,解释了神经元的信息处理机制,提出了量子神经元.该神经元对信息的处理分为两阶段.第1阶段为宏观信息收集部分,产生控制量子比特;第2阶段为微观信息处理部分,根据控制量子比特,改变神经元的状态.整个过程模拟量子受控非门.采用人工和实际数据集,作为分类研究对象,对比传统的神经元网络,量子神经元网络显示出较好的分类效果.以丙烯腈反应器作为建模研究对象,该网络显示出较强的泛化能力.
In the framework of quantum theory, the process of the neural unit dealing with information is explained, and quantum neural unit is proposed. The process includes two stages. The first stage is collecting macroscopical information, in which control quantum bit is produced. The second stage is dealing microcosmic information that neural unit state is changed according to control quantum bit. The whole process simulates the quantum controlled NOT gatce. The synthetic datasets and real datasets are used as classification objects. Classification results of the quantum neural network are better than that of the classical neural network. Additionally, acrylonitrile reaGor is used as modeling object. The proposed network shows the better performance.
出处
《控制与决策》
EI
CSCD
北大核心
2007年第9期1022-1026,共5页
Control and Decision
关键词
神经网络
量子神经计算
量子神经元
分类
建模
Neural network
Quantum neural computing
Quantum neural unit
Classification
Modeling
