Zhu Xiaorong

TL;DR: In this article, a network fault diagnosis method based on deep learning in a heterogeneous network environment is proposed, which comprises the following steps: first, determining features to be used in a monitoring phase and a fault diagnosis phase by analyzing a heterogenous network fault scene; then, selecting an optimal feature subset of the monitoring phase by using a minimum redundancy maximum correlation algorithm, matching a fault feature, if the matching degree is greater than a threshold, triggering the fault diagnosis, and completing model parameter adjustment through a counterpropagation algorithm; and finally, performing fault positioning and diagnosis on