Complex networks repair strategies: Dynamic models

2017 ◽  
Vol 482 ◽  
pp. 401-406 ◽  
Author(s):  
Chaoqi Fu ◽  
Ying Wang ◽  
Yangjun Gao ◽  
Xiaoyang Wang
Keyword(s):  
Complex Networks ◽  
Dynamic Models

scholarly journals Identification of dynamic models in complex networks with prediction error methods—Basic methods for consistent module estimates

Automatica ◽  
2013 ◽  
Vol 49 (10) ◽  
pp. 2994-3006 ◽  
Author(s):  
Paul M.J. Van den Hof ◽  
Arne Dankers ◽  
Peter S.C. Heuberger ◽  
Xavier Bombois
Keyword(s):  
Complex Networks ◽  
Prediction Error ◽  
Dynamic Models ◽  
Prediction Error Methods

scholarly journals Dynamical Analysis of Rumor Spreading Model considering Node Activity in Complex Networks

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Liang’an Huo ◽  
Fan Ding ◽  
Chen Liu ◽  
Yingying Cheng
Keyword(s):  
Complex Networks ◽  
Dynamic Models ◽  
Spreading Rate ◽  
Dynamical Analysis ◽  
Field Equations ◽  
Spreading Process ◽  
Rumor Spreading ◽  
Node Activity ◽  
Activity Rate ◽  
Mean Field Equations

The dynamic models are proposed to investigate the influence node activity has on rumor spreading process in both homogeneous and heterogeneous networks. Different from previous studies, we believe that the activity of nodes in complex networks affects the process of rumor spreading. An active node can have contact with all the nodes it directly links to, while an inactive node could only interact with its active neighbors. We explore the joint effort of activity rate, spreading rate and network topology on rumor spreading process by mean-field equations and numerical simulations, which reveals that there exists a critical curve consisting of critical activity rate and spreading rate; meanwhile, activity rate and spreading rate both have influence on the final rumor spreading scale.

scholarly journals Identification of Dynamic Models in Complex Networks With Prediction Error Methods: Predictor Input Selection

2016 ◽  
Vol 61 (4) ◽  
pp. 937-952 ◽  
Author(s):  
Arne Dankers ◽  
Paul M. J. Van den Hof ◽  
Xavier Bombois ◽  
Peter S. C. Heuberger
Keyword(s):  
Complex Networks ◽  
Prediction Error ◽  
Dynamic Models ◽  
Input Selection ◽  
Prediction Error Methods

Complex Networks

2009 ◽  
Author(s):  
Reuven Cohen ◽  
Shlomo Havlin
Keyword(s):  
Complex Networks

Monetary policy and the effect of the transfer of oil prices to inflation

2020 ◽  
pp. 41-50
Author(s):  
Ph. S. Kartaev ◽  
I. D. Medvedev
Keyword(s):  
Monetary Policy ◽  
Inflation Targeting ◽  
Dynamic Models ◽  
Oil Prices ◽  
Oil Price ◽  
Oil Price Shocks ◽  
Negative Effects ◽  
Price Shocks ◽  
Policy Regime ◽  
The Impact

The paper examines the impact of oil price shocks on inflation, as well as the impact of the choice of the monetary policy regime on the strength of this influence. We used dynamic models on panel data for the countries of the world for the period from 2000 to 2017. It is shown that mainly the impact of changes in oil prices on inflation is carried out through the channel of exchange rate. The paper demonstrates the influence of the transition to inflation targeting on the nature of the relationship between oil price shocks and inflation. This effect is asymmetrical: during periods of rising oil prices, inflation targeting reduces the effect of the transfer of oil prices, limiting negative effects of shock. During periods of decline in oil prices, this monetary policy regime, in contrast, contributes to a stronger transfer, helping to reduce inflation.

Methods of parallel computing for multilevel fuzzy Takagi – Sugeno systems

2016 ◽  
pp. 141-149
Author(s):  
S.V. Yershov ◽  
◽  
R.М. Ponomarenko ◽  
Keyword(s):  
Dynamic Models ◽  
Fuzzy Inference ◽  
Knowledge Bases ◽  
Comparative Characteristic ◽  
Software Systems ◽  
Scientific Papers ◽  
Speed Up ◽  
Diagnostic Software ◽  
Takagi Sugeno

Parallel tiered and dynamic models of the fuzzy inference in expert-diagnostic software systems are considered, which knowledge bases are based on fuzzy rules. Tiered parallel and dynamic fuzzy inference procedures are developed that allow speed up of computations in the software system for evaluating the quality of scientific papers. Evaluations of the effectiveness of parallel tiered and dynamic schemes of computations are constructed with complex dependency graph between blocks of fuzzy Takagi – Sugeno rules. Comparative characteristic of the efficacy of parallel-stacked and dynamic models is carried out.

A Comparison of Two Sub-structuring Techniques for Representing the Modal Properties of Tires

2001 ◽  
Vol 29 (1) ◽  
pp. 23-43 ◽  
Author(s):  
D. Tsihlas ◽  
T. Lacroix ◽  
B. Clayton
Keyword(s):  
Automobile Industry ◽  
Dynamic Models ◽  
Transfer Functions ◽  
Component Mode Synthesis ◽  
Numerical Techniques ◽  
The Past ◽  
Custom Made ◽  
Modal Behavior ◽  
Noise Vibration ◽  
Reduced Dynamic

Abstract Different numerical sub-structuring techniques for the representation of tire modal behavior have been developed in the past 20 years. By using these numerical techniques reduced dynamic models are obtained which can not only be used for internal studies but also be provided to the automobile industry and linked to reduced dynamic vehicle models in order to optimize the coupled vehicle-tire response for noise vibration and harshness purposes. Two techniques that have been developed in a custom-made finite element code are presented: 1) the component mode synthesis type models for which the wheel center interface is free and 2) the Craig and Bampton type models for which the wheel center interface is fixed. For both techniques the interface between the tire and the ground is fixed. The choice of fixed or free wheel center boundary condition is arbitrary. In this paper we will compare the formulation of these two numerical methods, and we will show the equivalency of both methods by showing the results obtained in terms of frequency and transfer functions. We will show that the two methods are equivalent in principle and the reduced dynamic models can be converted from one to the other. The advantages-disadvantages of each method will be discussed along with a comparison with experimentally obtained results.

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