基于粒子群优化的核主元分析特征的提取技术

针对核主元分析在参数设置上的盲目性,提出应用粒子群优化算法优化核函数参数.并将核主元分析应用于特征提取中.首先建立核函数参数优化的数学模型,然后应用加速度自适应粒子群优化算法对其寻优,并通过Iris数据集进行仿真研究,验证其提取特征的有效性.将优化的核主元分析方法应用于齿轮箱典型故障的特征提取中,结果表明:参数优化的核主元分析能有效降低齿轮箱特征向量的维数,较线性主元分析取得更好的故障识别效果.该方法在机械故障信号的非线性特征提取中具有优势.

Feature Extraction Based on Kernel Principal Component Analysis Optimized by Particle Swarm Optimization Algorithm

Aimed at the blind setting of parameter in kernel principal component analysis (KPCA), kernel function parameter optimized by particle swarm optimization algorithm (PSO) was proposed, and KPCA was applied to feature extraction. An objective function model of kernel function parameter was constructed firstly, then a particle swarm optimization algorithm with adaptive accelerate (CPSO) was used to optimize it, and the iris data were applied to the optimization method for simulation, which testified the KPCA effectivity in feature extraction. The optimized KPCA was applied to feature extraction of typical gearbox faults. The results indicate that the optimized KPCA can effectively reduce the dimension of feature vector of gearbox, and it has a better fault classification performance than linear principal component analysis (PCA). This method has an advantage in nonlinear feature extraction of mechanical failure signal.