A Novel Hybrid CNN-LSTM Compensation Model Against DoS Attacks in Power System State Estimation-Reference-Cited by-同舟云学术

A Novel Hybrid CNN-LSTM Compensation Model Against DoS Attacks in Power System State Estimation

Published:2022-01-17 Issue:3 Volume:54 Page:1597-1621
ISSN:1370-4621
Container-title:Neural Processing Letters
language:en
Short-container-title:Neural Process Lett

Author:

Xu Xiaoxin,Sun Jian,Wang Chunye,Zou Bin

Funder

innovative research group project of the national natural science foundation of china

fundamental research funds for central universities of the central south university

chongqing special fund for technological innovation and application development

Natural Science Foundation of Chongqing

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Networks and Communications,General Neuroscience,Software

Link

https://link.springer.com/content/pdf/10.1007/s11063-021-10696-3.pdf

Reference47 articles.

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3. Ali MS, Gunasekaran N, Joo YH (2018) Sampled-data state estimation of neutral type neural networks with mixed time-varying delays[J]. Neural Process Lett 50:357–378

4. Ali MS, Gunasekaran N, Zhu Q (2017) State estimation of T-S fuzzy delayed neural networks with Markovian jumping parameters using sampled-data control. Fuzzy Sets Syst 306:87–104

5. Teixeira A, Amin S, Sandberg H, Johansson KH, Sastry SS (2010) Cyber security analysis of state estimators in electric power systems. In: 49th IEEE conference on decision and control (CDC), pp 5991–5998

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