光电测量系统故障诊断中跟踪误差预测的CS-BP算法研究

Research on CS-BP algorithm of tracking error prediction in fault diagnosis of photoelectric measurement system

近年来,随着光电测量系统的数量与复杂度的日趋增长,其故障诊断的需求也不断增加。在光电测量系统的故障诊断中,跟踪误差的预测尤为重要。本文在BP神经网络的基础上利用布谷鸟算法进行了阈值及权值的优化,提出了一种CS-BP算法。利用光电测量系统的方位引导、俯仰引导、方位编码器、俯仰编码器和时间数据,对跟踪误差进行预测。与传统神经网络算法相比,该算法利用布谷鸟出色的寻找极值特点,解决了因初始阈值及权值设置不当给神经网络算法所带来的无法得到最优解的问题。实验结果表明,与传统BP神经网络、遗传算法优化的BP神经网络(GA-BP)对比,CS-BP算法的迭代次数分别少21次和60次,且其预测平均相对误差分别低4.85%和1.57%。因此,CS-BP算法具有较快的收敛速度和较高的预测精度,适合应用在光电测量系统故障诊断中。

In recent years,with the increasing number and complexity of photoelectric measurement systems,the demand for fault diagnosis is also increasing.In the fault diagnosis of the photoelectric measurement system,the prediction of its tracking error is particularly important.In this paper,we propose a BP neural network algorithm optimized by the Cuckoo algorithm(CS-BP).The tracking error can be predicted by using the azimuth guidance,pitch guidance,azimuth encoder,pitch encoder and time data of the optoelectronic measurement system.Compared with the traditional neural network algorithm,this algorithm uses the excellent characteristics of Cuckoo to find the extreme value,and solves the problem that the neural network algorithm cannot get the optimal solution due to the improper setting of the initial threshold and weight.The experimental results show that,the number of iterations with CS-BP is 21 and 60 less than the traditional BP neural network and the BP neural network optimized by the genetic algorithm(GA-BP),respectively.The relative errors are 4.85% and 1.57% lower,respectively.Therefore,the CS-BP algorithm has a faster convergence speed and higher prediction accuracy,and it is suitable for fault diagnosis of photoelectric measurement system.