量子神经网络软测量模型及应用

提出了一种基于量子神经网络的软测量模型,组成该模型的量子神经元对信息的处理分为两阶段。第一阶段为宏观信息收集部分,产生控制量子比特;第二阶段为微观信息处理部分,根据控制量子比特,改变工作量子比特,即神经元的状态,整个过程模拟量子受控非门。以某石化厂乙烯收率为软测量对象,实验结果显示出,提出的量子神经网络软测量模型可以较好地跟踪乙烯收率的变化。

Soft Sensing Model Based on Quantum Neural Network and Its Application

A soft sensing model was proposed based on quantum neural network. The quantum neural network consists of several quantum neural units. The information processing of the neural units includes two stages. Firstly, macroscopical information is collected to produce control quantum bit. Secondly, neural unit state is changed according to control quantum bit. The whole process simulates quantum controlled NOT gate. Ethylene yield was used as soft sensing object to show the effectiveness of the proposed method. Experimental results argue that the novel soft sensing model could trail the yields change precisely.