Modeling and analysis of the ocean dynamic with Gaussian complex network

The aim of this paper is to contribute to the modeling and analysis of complex systems, taking into account the nature of complexity at different stages of the system life-cycle: from its genesis to its evolution. Therefore, some structural aspects of the complexity dynamics are highlighted, leading (i) to implement the morphogenesis of emergent complex network structures, and (ii) to control some synchronization phenomena within complex networks. Specific applications are proposed to illustrate these two aspects, in urban dynamics and in neural networks.

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Complex Network Based Modeling and Analysis of Resource Nodes for MG

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.619.332 ◽

2014 ◽

Vol 619 ◽

pp. 332-336 ◽

Cited By ~ 1

Author(s):

Yong Yin ◽

Chao Yong Zhang ◽

De Jun Chen

Keyword(s):

Complex Network ◽

Network Model ◽

Element Model ◽

Optimal Composition ◽

Manufacturing Grid ◽

Modeling And Analysis ◽

Static Parameter ◽

Scale Free ◽

Manufacturing Resource ◽

Synchronization Performance

Manufacturing Grid (MG) is one of the most promising manufacturing modes in recent years, which has been proved to be a complex network characterized with scale-free. In this paper, the resource nodes of MG are treated as the nodes of a network and the linkage among each node is treat as the edges or arcs, based on which a three-element model of G = ( V, E, R) is proposed, and a scene of the optimal composition strategy among the 50 candidate nodes aiming at task A is modeled. Next the static parameter of the degree for a resource node and the synchronization performance of the complex network model for the resource nodes are studied. Results of the paper can promote the management of the manufacturing resource nodes and improve the efficiency of sharing MG resources, so as to facilitate the application and popularization of MG.

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Reliability modeling and analysis of cloud service based on complex network

2016 Prognostics and System Health Management Conference (PHM-Chengdu) ◽

10.1109/phm.2016.7819907 ◽

2016 ◽

Cited By ~ 1

Author(s):

Yaxin Bai ◽

Hong Zhang ◽

Yangzhen Fu

Keyword(s):

Complex Network ◽

Cloud Service ◽

Reliability Modeling ◽

Modeling And Analysis

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Data-driven modeling and analysis based on complex network for multimode recognition of industrial processes

Journal of Manufacturing Systems ◽

10.1016/j.jmsy.2021.04.001 ◽

2021 ◽

Author(s):

Yan-Ning Sun ◽

Zi-Long Zhuang ◽

Hong-Wei Xu ◽

Wei Qin ◽

Meng-Jiao Feng

Keyword(s):

Complex Network ◽

Data Driven ◽

Industrial Processes ◽

Modeling And Analysis ◽

Data Driven Modeling

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MODELING AND ANALYSIS OF BIOPATHWAYS DYNAMICS

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720012310014 ◽

2012 ◽

Vol 10 (04) ◽

pp. 1231001 ◽

Cited By ~ 7

Author(s):

BING LIU ◽

P. S. THIAGARAJAN

Keyword(s):

Sensitivity Analysis ◽

Parameter Estimation ◽

Differential Equations ◽

Ordinary Differential Equations ◽

Complex Network ◽

Biochemical Networks ◽

Approximation Technique ◽

Modeling And Analysis ◽

Cellular Processes ◽

Probabilistic Approximation

Cellular processes are governed and coordinated by a multitude of biopathways. A pathway can be viewed as a complex network of biochemical reactions. The dynamics of this network largely determines the functioning of the pathway. Hence the modeling and analysis of biochemical networks dynamics is an important problem and is an active area of research. Here we review quantitative models of biochemical networks based on ordinary differential equations (ODEs). We mainly focus on the parameter estimation and sensitivity analysis problems and survey the current methods for tackling them. In this context we also highlight a recently developed probabilistic approximation technique using which these two problems can be considerably simplified.

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