How to be a gray box: dynamic semi-physical modeling

Oussar, Yacine; Dreyfus, Gérard

doi:10.1016/s0893-6080(01)00096-x

Cited by 72 publications

(50 citation statements)

References 2 publications

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“…On the one hand, although the electrochemical, thermodynamic, and transport phenomena that define the behaviour of a Li-Ion battery are well known [16], white models are not valid because these models depend on a large number of parameters that are not provided by the manufacturer [17]. On the other hand, black boxes are unreliable when predicting unforeseen states and cannot incorporate prior knowledge about the mentioned phenomena [18]. A balance between white and black boxes is needed, and these are the "grey boxes," also known as "semiphysical models.…”

Section: State Of the Art In The State Of Health Monitoringmentioning

confidence: 99%

A Model-Based Virtual Sensor for Condition Monitoring of Li-Ion Batteries in Cyber-Physical Vehicle Systems

et al. 2017

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A model-based virtual sensor for assessing the health of rechargeable batteries for cyber-physical vehicle systems (CPVSs) is presented that can exploit coarse data streamed from on-vehicle sensors of current, voltage, and temperature. First-principle-based models are combined with knowledge acquired from data in a semiphysical arrangement. The dynamic behaviour of the battery is embodied in the parametric definition of a set of differential equations, and fuzzy knowledge bases are embedded as nonlinear blocks in these equations, providing a human understandable reading of the State of Health of the CPVS that can be easily integrated in the fleet through-life management.

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Section: State Of the Art In The State Of Health Monitoringmentioning

confidence: 99%

A Model-Based Virtual Sensor for Condition Monitoring of Li-Ion Batteries in Cyber-Physical Vehicle Systems

et al. 2017

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“…Introduced in the case of artificial neural networks, the semi-physical modeling consists in combining the flexibility of a behavior model implemented by learning mechanisms with the legibility of a knowledge-based model [42,43]. Indeed, very frequently, and especially in manufacturing, it is inconceivable to find a knowledge-based (or white-box) model which is satisfactory for the purpose of interest (i.e.…”

Section: Semi-physical Dynamic Modelingmentioning

confidence: 99%

“…A general methodology for designing semi-physical models was proposed in [42]. The first step consisted in implementing as discrete-time neural network with fixed weights the functions which known reliably.…”

Section: Semi-physical Dynamic Modelingmentioning

confidence: 99%

Estimation of a semi-physical GLBE model using dual EnKF learning algorithm coupled with a sensor network design strategy: Application to air field monitoring

Roussel¹,

Bourgois

Benjelloun³

et al. 2013

Information Fusion

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Delmaire. Estimation of a semiphysical GLBE model using dual EnKF learning algorithm coupled with a sensor network design strategy: application to air field monitoring. Information Fusion, Elsevier, 2013, 14, pp.335-348. <10.1016/j.inffus.2013.03.001>. computation time was used by combining two filters consecutively activated in the same iteration. Finally, we proposed to complete the lack of spatial information of the sparse-observation network by adding a mobile sensor, which was routed to the location where the cell-by-cell output estimation error was the highest. Experimental results in the context of the standard lid-driven cavity problem revealed the presence of few zones of interest, where fixed sensors can be deployed to increase performances in terms of convergence speed and estimation quality. Finally, the study showed the feasibility of introducing some additional parameters which act as degrees of freedom, to perform large-eddy simulation of turbulent flows without numerical instabilities.

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“…Between these two methods, there is another approach which is much more closer to the notion of model fusion. This approach consists in modifying the design of a recurrent neural network (Oussar & Dreyfus, 2001;Ploix & Dreyfus, 1997). The idea is to design a recurrent neural network using engineer's knowledge on the fundamental laws which govern the system.…”

Section: Introductionmentioning

confidence: 99%

“…One or more degrees of freedom (e.g. additional neurons) may also be added to help the network successfully adapt to the ignored parts of the system (Oussar & Dreyfus, 2001). Process measurements are then used to learn the network.…”

Section: Introductionmentioning

confidence: 99%

Semi-physical neural modeling for linear signal restoration

Bourgois¹,

Roussel²,

Benjelloun³

2013

Neural Networks

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This paper deals with the design methodology of an Inverse Neural Network (INN) model. The basic idea is to carry out a semi-physical model gathering two types of information: the a priori knowledge of the deterministic rules which govern the studied system and the observation of the actual conduct of this system obtained from experimental data. This hybrid model is elaborated by being inspired by the mechanisms of a neuromimetic network whose structure is constrained by the discrete reverse-time state-space equations. In order to validate the approach, some tests are performed on two dynamic models. The first suggested model is a dynamic system characterized by an unspecified r-order Ordinary Differential Equation (ODE). The second one concerns in particular the mass balance equation for a dispersion phenomenon governed by a Partial Differential Equation (PDE) discretized on a basic mesh. The performances are numerically analyzed in terms of generalization, regularization and training effort.

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How to be a gray box: dynamic semi-physical modeling

Cited by 72 publications

References 2 publications

A Model-Based Virtual Sensor for Condition Monitoring of Li-Ion Batteries in Cyber-Physical Vehicle Systems

A Model-Based Virtual Sensor for Condition Monitoring of Li-Ion Batteries in Cyber-Physical Vehicle Systems

Estimation of a semi-physical GLBE model using dual EnKF learning algorithm coupled with a sensor network design strategy: Application to air field monitoring

Semi-physical neural modeling for linear signal restoration

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