摘要
为提高抽油机故障诊断效率,提出了在布谷鸟搜索算法(CS:Cuckoo Search)中加入自适应步长,并用函数进行测试,结果验证了改进算法的有效性。用改进的CS算法优化BP(Back Propagation)神经网络的权值和阈值,并与传统BP算法进行比较,证明了改进的CS算法克服了传统算法训练速度慢、易陷局部极值的缺点。将优化的神经网络应用于抽油机故障诊断中的实验表明,该算法具有较快的收敛速度和较好的稳定性,同时也提高了抽油机故障诊断的精确性。
In order to solve the problem of slow convergent speed and poor local search ability of CS（ Cuckoo Search） algorithm,a self-adaptive step is introduced in CS,which balances the global and local search ability and improves the convergent speed. Experiments have been conducted on functions. The results indicate the good performance of the new algorithm. Initial weights and threshold values are optimized by the modified CS. Compared with the conventional BP（ Back Propagation） algorithm,the condition of slow training speed and premature result is released. The new neural network is used in pump units’ fault diagnosis. The result shows that CS-BP enhances the convergence rate and robust of neural network,and the accuracy of the fault diagnosis.
出处
《吉林大学学报（信息科学版）》
CAS
2017年第3期324-332,共9页
Journal of Jilin University（Information Science Edition）
基金
国家自然科学基金资助项目(61374127)
黑龙江省博士后科研启动基金资助项目(LBH-Q12143)
黑龙江省青年基金资助项目(QC2013C066)
关键词
自适应步长
布谷鸟搜索算法
神经网络
故障诊断
self-adaptive step
cuckoo search algorithm
neural network
fault diagnosis
