Continuous-time Dynamic Network Node Classification Algorithm Based on Autoencoder

2020 ◽  
Author(s):  
Xin Huang ◽  
Yun Li ◽  
JinYu Xiong ◽  
Jie Liang ◽  
Jie Wu ◽  
...  
Keyword(s):  
Continuous Time ◽  
Dynamic Network ◽  
Classification Algorithm ◽  
Network Node ◽  
Time Dynamic ◽  
Node Classification

scholarly journals On solving continuous-time dynamic network flows

2011 ◽  
Vol 53 (3) ◽  
pp. 497-524 ◽  
Author(s):  
S. Mehdi Hashemi ◽  
Ebrahim Nasrabadi
Keyword(s):  
Continuous Time ◽  
Network Flows ◽  
Dynamic Network ◽  
Time Dynamic ◽  
Dynamic Network Flows

scholarly journals Continuous-Time Dynamic Network Embeddings

2018 ◽  
Author(s):  
Giang Hoang Nguyen ◽  
John Boaz Lee ◽  
Ryan A. Rossi ◽  
Nesreen K. Ahmed ◽  
Eunyee Koh ◽  
...  
Keyword(s):  
Continuous Time ◽  
Dynamic Network ◽  
Time Dynamic

Hierarchical Bayesian continuous time dynamic modeling.

2018 ◽  
Vol 23 (4) ◽  
pp. 774-799 ◽  
Author(s):  
Charles C. Driver ◽  
Manuel C. Voelkle
Keyword(s):  
Dynamic Modeling ◽  
Continuous Time ◽  
Hierarchical Bayesian ◽  
Time Dynamic

Modelling of Continuous-Time Dynamic Systems

2007 ◽  
pp. 249-273
Author(s):  
Louis G. Birta ◽  
Gilbert Arbez
Keyword(s):  
Dynamic Systems ◽  
Continuous Time ◽  
Time Dynamic

scholarly journals A Simple And Novel Algorithm For State Estimation Of Continuous Time Linear Stochastic Dynamic Systems Excited By Random Inputs

2019 ◽  
Vol 8 (10) ◽  
pp. 3559-3563
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Continuous Time ◽  
Stochastic Dynamic ◽  
Time Dynamic ◽  
Time Dynamics ◽  
Hardware Realization ◽  
Aerial Vehicle ◽  
Non Linear System ◽  
Stochastic Dynamic Systems

The major goal of this paper is to explore the effective state estimation algorithm for continuous time dynamic system under the lossy environment without increasing the complexity of hardware realization. Though the existing methods of state estimation of continuous time system provides effective estimation with data loss, the real time hardware realization is difficult due to the complexity and multiple processing. Kalman Filter and Particle Filer are fundamental algorithms for state estimation of any linear and non-linear system respectively, but both have its limitation. The approach adopted here, detect the expected state value and covariance, existed by random input at each stage and filtered the noisy measurement and replace it with predicted modified value for the effective state estimation. To demonstrate the performance of the results, the continuous time dynamics of position of the Aerial Vehicle is used with proposed algorithm under the lossy measurements scenario and compared with standard Kalman filter and smoothed filter. The results show that the proposed method can effectively estimate the position of Aerial Vehicle compared to standard Kalman and smoothed filter under the non-reliable sensor measurements with less hardware realization complexity.

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