@inproceedings{51ffcc2e068e4d1298626eb2129c00fb,
title = "Topology estimation of uncertain general complex dynamical networks from noisy time series",
abstract = "This paper addresses the problem of simultaneous estimation of the topological structure and unknown parameters of uncertain general complex networks from noisy time series. Usually the complex networks consist of known node models with some unknown parameters and uncertain topological structure. At the same time, only partial states with heavy noise can be observed in real-world complex networks. By means of the unscented Kalman filter (UKF), we estimate the unknown states, parameters as well as topological structure with high accuracy only from partial heavily noise-corrupted states of the nodes. The simulation results verify the effectiveness of the proposed approach.",
author = "Che, {Yan Qiu} and Jiang Wang and Cui, {Shi Gang} and Li Zhao and Bin Deng and Wei, {Xi Le}",
year = "2011",
doi = "10.1109/ICMTMA.2011.828",
language = "English (US)",
isbn = "9780769542966",
series = "Proceedings - 3rd International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2011",
pages = "1031--1034",
booktitle = "Proceedings - 3rd International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2011",
note = "3rd International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2011 ; Conference date: 06-01-2011 Through 07-01-2011",
}