基于量子蛙跳算法和对传神经网络的油井故障诊断

A Method for Well Failure Diagnosis Based on Quantum Frog Leaping Algorithm and Counter Propagation Network

构建了一种改进对传神经网络,提出一种量子蛙跳算法,采用概率幅作为个体编码,利用量子旋转门来实现个体更新,量子非门实现个体变异,用小波包提取各频段的能量组成特征向量作为网络输入,通过神经网络输出结果判别油井的故障类型。仿真试验结果表明,该方法可以得到更好的分类结果,有一定的应用价值。

In this paper,an improved counter propagation network is built,and a quantum frog leaping learning algorithm is proposed.The individual encodes for probability amplitudes of quantum bits,the updates of individual are performed by quantum rotation gates,the mutations of individual are performed by quantum non-gate.Extracting the feature vectors composed of energy of different bands constitute network input.The types of well fault are judged by output results of neural network.The results of simulation experiments show that the method can get better classification results,it has a certain application value.