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 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

Parameter Adaptation by Parameter Signature Isolation in the Time-Scale Domain

2008 ◽  
Author(s):  
Kourosh Danai ◽  
James R. McCusker
Keyword(s):  
Error Estimates ◽  
Time Scale ◽  
Prediction Error ◽  
Dynamic Models ◽  
Noise Suppression ◽  
Model Parameters ◽  
Isolation Method ◽  
Parameter Adaptation ◽  
Single Output ◽  
Parameter Error

It is shown that delineation of output sensitivities with respect to model parameters in dynamic models can be enhanced in the time-scale domain. This enhanced differentiation of output sensitivities then provides the capacity to isolate regions of the time-scale plane wherein a single output sensitivity dominates the others. Due to this dominance, the prediction error can be attributed to the error of a single parameter at these regions so as to estimate each model parameter error separately. The proposed Parameter Signature Isolation Method (PARSIM) that uses these parameter error estimates for parameter adaptation has been found to have an adaptation precision comparable to that of the Gauss-Newton method for noise-free cases. PARSIM, however, appears to be less sensitive to input conditions, while offering the promise of more effective noise suppression by the capabilities available in the time-scale domain.

Prediction error methods in learning jump ARMAX models

2018 ◽  
Author(s):  
Valentine Breschi ◽  
Alberto Bemporad ◽  
Dario Piga ◽  
Stephen Boyd
Keyword(s):  
Prediction Error ◽  
Prediction Error Methods

Validation of Dynamic Models by Image Distances in the Time-Scale Domain

2009 ◽  
Author(s):  
Kourosh Danai ◽  
James R. McCusker ◽  
Todd Currier ◽  
David O. Kazmer
Keyword(s):  
Time Series ◽  
Dynamic Model ◽  
Model Validation ◽  
Time Scale ◽  
Prediction Error ◽  
Dynamic Models ◽  
Traditional Approaches

Model validation is the procedure whereby the fidelity of a model is evaluated. The traditional approaches to dynamic model validation either rely on the magnitude of the prediction error between the process observations and model outputs or consider the observations and model outputs as time series and use their similarity to assess the closeness of the model to the process. Here, we propose transforming these time series into the time-scale domain, to enhance their delineation, and using image distances between these transformed time series to assess the closeness of the model to the process. It is shown that the image distances provide a more consistent measure of model closeness than available from the magnitude of the prediction error.

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.

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 Dynamic network structure identification with prediction error methods - basic examples

2012 ◽  
Vol 45 (16) ◽  
pp. 876-881 ◽  
Author(s):  
Arne G. Dankers ◽  
Paul M.J. Van den Hof ◽  
Peter S.C. Heuberger ◽  
Xavier Bombois
Keyword(s):  
Network Structure ◽  
Prediction Error ◽  
Dynamic Network ◽  
Structure Identification ◽  
Prediction Error Methods
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