Evaluation of hidden danger types of optical channel performance degradation based on machine learning cascading technology
by Qing Wang; Qiong Cheng; Yuzhi Jing; Shuxin Nie; Jun Yang
International Journal of Embedded Systems (IJES), Vol. 16, No. 3, 2023

Abstract: In order to improve the accuracy of performance degradation hazard type assessment and improve the transmission effect of optical channel signal, this paper uses machine learning cascade technology to conduct in-depth research on optical channel performance degradation hazard type assessment. This paper firstly analyses the causes and characteristics of potential degradation hazards of optical channel (OC) performance, and then classifies OC performance degradation hazards by using machine learning algorithm. Meanwhile, in order to verify the effectiveness of the machine learning cascade technology, this paper takes the real OC performance degradation data of an optical communication enterprise as a sample set to conduct precision experiment analysis. The results show that the ML algorithm can effectively and accurately classify the potential degradation hazards of OC performance. By concatenating decision tree, support vector machine and neural network, the accuracy of identifying potential degradation hazards of OC performance can reach 92.37%.

Online publication date: Mon, 10-Jun-2024

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