机械制造与自动化Issue(5)：103-106,4.
基于KPCA和BPNN的模拟加载系统油压信号识别技术研究
Research on Oil Pressure Signal Recognition of Simulated Loading System Based on KPCA-BPNN
摘要
Abstract
To solve the signal recognition of the simulated loading system, this paper proposes a recognition method based on Ker-nel Principal Component Analysis ( KPCA) feature extraction and BP Neural Network ( BPNN) . The KPCA is applied to the data ex-traction of the original samples and then, the BP neutral network pattern classifier is used to identify six different working states of the device. The test results verifies the effectiveness of the method above, and some useful references are proveded for the characteris-tic analysis and pattern recognition of the similar hydraulic pressure signals.关键词
工程机械/模拟加载/油压信号/核主元分析/BP神经网络Key words
engineering machinery/simulated loading/oil pressure signal/Kernel Principal Component Analysis/BP Neural Net-work分类
信息技术与安全科学引用本文复制引用
王东,王新晴,段金辉,梁升,沈新民..基于KPCA和BPNN的模拟加载系统油压信号识别技术研究[J].机械制造与自动化,2016,(5):103-106,4.基金项目
国家自然科学基金资助项目(51505498) （51505498）
