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

大型复杂装备的工作过程均表现出较强的非线性,并且受非高斯噪声和各种不确定因素的影响,导致状态监测信息多是高维的非线性、非高斯数据,且计算量随信息维数呈指数增长,若直接用于预测模型则导致计算量异常庞大,不利于完成模型参数估计和实现实时维修。针对上述问题,对核独立分量分析算法中关于核函数参数选择的盲目性,提出了用粒子群优化算法改进核参数选择过程的核独立分量分析算法,实现了高维状态信息的降维。最后,通过对某自行火炮发动机油液监测数据进行特征降维实例分析,验证了所提方法的可行性与有效性。

Feature dimension reduction of kernel independent component by particle swarm optimization and its application

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.