High Dimensional Electromagnetic Interference Signal Clustering Based On SOM Neural Network

Abstract

In this paper, we study the spectral characteristics and global representations of strongly nonlinear, non-stationary electromagnetic interferences (EMI), which is of great significance in analysing the mathematical modelling of electromagnetic capability (EMC) for a large scale integrated system. We firstly propose to use Self-Organizing Feature Map Neural Network (SOM) to cluster EMI signals. To tackle with the high dimensionality of EMI signals, we combine the dimension reduction and clustering approaches, and find out the global features of different interference factors, in order to finally provide precise mathematical simulation models for EMC design, analysis, forecasting and evaluation. Experimental results have demonstrated the validity and effectiveness of the proposed method.