摘要
为解决电子系统健康状态监测数据的冗余性和高维性问题,提出了一种将样本优化和特征优化相结合的监测数据优化算法。首先,采用特征空间样本选择算法对监测数据进行样本优化,找出最具代表性的样本;然后,采用核主成分分析—分布估计算法(KPCA-EDA)对样本优化后的监测数据进行特征优化,在保证特征信息充足的情况下,保留更多的识别信息;最后,以某滤波电路为例进行了验证,仿真结果表明,该算法同KPCA等优化算法相比,在训练时间和识别率上能达到更好的平衡。
To solve the redundancy and high-dimensional problem of the electronic system condition monitoring data, a monitoring data optimization algorithm that combined the sample optimization and features optimization was put forward. Firstly, monitoring data samples were optimized by feature space sample selection algorithm, and the most representative samples were found; then monitoring data characteristics were optimized by KPCA-EDA algorithm after the sample optimization. More recognition information was retained on guarantee that the feature information was enough. Finally, a filter circuit was taken as an example to simulate, and the result shows that this method is effective.
出处
《计算机应用》
CSCD
北大核心
2012年第10期2927-2930,共4页
journal of Computer Applications
基金
总装备部科研项目
总装备部预研项目
关键词
电子系统
监测数据优化
特征空间样本选择
核主成分分析
分布估计算法
electronic system
monitoring data optimization
feature space sample selection
Kernel Principal Component Analysis （KPCA）
Estimation of Distribution Algorithms （EDA）
