LM-BP based operation quality assessment method for OTN in Smart grid

Wu, Manman; Guo, Shaoyong; Chen, Xingyu; Xing, Ningzhe; Zhong, Cheng

doi:10.1109/apnoms.2016.7737257

Cited by 3 publications

(2 citation statements)

References 3 publications

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“…Therefore, machine learning aims to facilitate decision-making more powerfully. It can be applied in different areas and can reduce costs directly or indirectly with the primary objective of improving productivity [48]. Within this context, only two papers used machine learning for OTN network planning or to assist during the decision process in optical network planning.…”

Section: Discussionmentioning

confidence: 99%

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Systematic Review on Methods for OTN Network Planning

Silva

Souza

Ribeiro

et al. 2023

Preprint

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This paper presents a systematic review applying the PRISMA methodology with a Systematic Review Protocol (SRP) on the definitions of methods, techniques, and algorithms used for optical transport network (OTN) planning. OTN has been extensively used worldwide, and many issues still need to be studied. We conducted a comprehensive review of the relevant studies to provide insight into the recent progress and the research gaps in the field. According to the analyzed studies, research questions and open issues are associated with using Machine learning, Resilience, and Energy Consumption. These approaches are highlighted as future research directions to enable the development of OTN planning.

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Section: Discussionmentioning

confidence: 99%

“…Among them, 02 articles [44], [19] use decision trees that present multi-layer traffic preparation algorithms for a network. The other one [48] uses ML-BP based on an artificial neural network to optimize the path standard and define QoS advantages with choose.…”

Section: Machine Learningmentioning

confidence: 99%

Systematic Review on Methods for OTN Network Planning

Silva

Souza

Ribeiro

et al. 2023

Preprint

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Review on Evaluation of Power Communication Network, Dispatching Automation System and Distribution Automation System

Liu,

Wang,

Xin

et al. 2023

2023 8th International Conference on Power and Renewable Energy (ICPRE)

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Adaptive Moment Estimation To Minimize Square Error In Backpropagation Algorithm

Singarimbun

2020

Data Science: J. of Computing and Appl. Informatics

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Back - propagation Neural Network has weaknesses such as errors of gradient descent training slowly of error function, training time is too long and is easy to fall into local optimum. Back - propagation algorithm is one of the artificial neural network training algorithm that has weaknesses such as the convergence of long, over-fitting and easy to get stuck in local optima. Back - propagation is used to minimize errors in each iteration. This paper investigates and evaluates the performance of Adaptive Moment Estimation (ADAM) to minimize the squared error in back - propagation gradient descent algorithm. Adaptive Estimation moment can speed up the training and achieve the level of acceleration to get linear. ADAM can adapt to changes in the system, and can optimize many parameters with a low calculation. The results of the study indicate that the performance of adaptive moment estimation can minimize the squared error in the output of neural networks.

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LM-BP based operation quality assessment method for OTN in Smart grid

Cited by 3 publications

References 3 publications

Systematic Review on Methods for OTN Network Planning

Systematic Review on Methods for OTN Network Planning

Review on Evaluation of Power Communication Network, Dispatching Automation System and Distribution Automation System

Adaptive Moment Estimation To Minimize Square Error In Backpropagation Algorithm

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