Connectivity and Competition in Airline Networks: A Complexity Analysis of Lufthansa's Network

2011 ◽  
Author(s):  
Aura Reggiani ◽  
Peter Nijkamp ◽  
Alessandro Cento
Keyword(s):  
Complexity Analysis ◽  
Airline Networks

scholarly journals Connectivity and concentration in airline networks: a complexity analysis of Lufthansa's network

2010 ◽  
Vol 19 (4) ◽  
pp. 449-461 ◽  
Author(s):  
Aura Reggiani ◽  
Peter Nijkamp ◽  
Alessandro Cento
Keyword(s):  
Complexity Analysis ◽  
Airline Networks

The Complexity Analysis Tool

1988 ◽  
Author(s):  
Paul E. Janusz
Keyword(s):  
Complexity Analysis ◽  
Analysis Tool

Models of network formation

2018 ◽  
Author(s):  
Mark Newman
Keyword(s):  
Degree Distribution ◽  
Network Formation ◽  
Preferential Attachment ◽  
Network Size ◽  
Large Network ◽  
Citation Networks ◽  
Airline Networks ◽  
Attachment Model ◽  
Delivery Networks

This chapter describes models of the growth or formation of networks, with a particular focus on preferential attachment models. It starts with a discussion of the classic preferential attachment model for citation networks introduced by Price, including a complete derivation of the degree distribution in the limit of large network size. Subsequent sections introduce the Barabasi-Albert model and various generalized preferential attachment models, including models with addition or removal of extra nodes or edges and models with nonlinear preferential attachment. Also discussed are node copying models and models in which networks are formed by optimization processes, such as delivery networks or airline networks.

scholarly journals Complexity Analysis of an Edge Preserving CNN SAR Despeckling Algorithm

2020 ◽  
Author(s):  
Sergio Vitale ◽  
Giampaolo Ferraioli ◽  
Vito Pascazio
Keyword(s):  
Complexity Analysis ◽  
Edge Preserving

Centered error entropy Kalman filter with application to satellite attitude determination

2021 ◽  
pp. 014233122110198
Author(s):  
Baojian Yang ◽  
Lu Cao ◽  
Dechao Ran ◽  
Bing Xiao
Keyword(s):  
Kalman Filter ◽  
Gaussian Noise ◽  
Attitude Determination ◽  
Complexity Analysis ◽  
Iteration Algorithm ◽  
Robust Algorithms ◽  
Convergence Conditions ◽  
Satellite Attitude ◽  
Heavy Tailed ◽  
Non Gaussian

Due to unavoidable factors, heavy-tailed noise appears in satellite attitude estimation. Traditional Kalman filter is prone to performance degradation and even filtering divergence when facing non-Gaussian noise. The existing robust algorithms have limited accuracy. To improve the attitude determination accuracy under non-Gaussian noise, we use the centered error entropy (CEE) criterion to derive a new filter named centered error entropy Kalman filter (CEEKF). CEEKF is formed by maximizing the CEE cost function. In the CEEKF algorithm, the prior state values are transmitted the same as the classical Kalman filter, and the posterior states are calculated by the fixed-point iteration method. The CEE EKF (CEE-EKF) algorithm is also derived to improve filtering accuracy in the case of the nonlinear system. We also give the convergence conditions of the iteration algorithm and the computational complexity analysis of CEEKF. The results of the two simulation examples validate the robustness of the algorithm we presented.

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