河北科技大学学报2013，Vol.34Issue(1)：60-66,7.DOI:10.7535/hbkd.2013yx01013
基于粒子群优化核独立分量的特征降维算法及其应用研究
Feature dimension reduction of kernel independent component by particle swarm optimization and its application
摘要
Abstract
The operating process of complex equipment has strong non-linearity, and it is often affected by some unknown factors, bringing much non-linear and non-gaussion monitoring data, and the calculation time grows up like exponential form as calculated amount increases. If these data are used directly for equipment residual life prediction, it is hard to complete model parameters' estimation and realize equipment's online maintenance. Aiming at settling the above problems, especially for the blindness of kernel function parameters selection in kernel independent component analysis, the kernel function parameters are optimized by particle swarm optimization arithmetic to reduce feature dimension. Finally, the oil monitoring data of self-propelled gun engine is used for dimension reduction. Testing results show the feasibility and effects of the proposed method.关键词
粒子群算法/核独立分量分析/特征降维/油液光谱分析Key words
particle swarm optimization/ kernel independent component analysis/ feature dimension reducing/ oil spectrum analysis分类
机械制造引用本文复制引用
孙磊,贾云献,王卫国,张英波,赵劲松..基于粒子群优化核独立分量的特征降维算法及其应用研究[J].河北科技大学学报,2013,34(1):60-66,7.基金项目
总装备部重点预研基金资助项目(9140A27020308JB34) （9140A27020308JB34）
