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DOI: 10.1615/ICHMT.2000.TherSieProcVol2.360
107 pages

Sylvain Lalot
M.E.T.I.E.R., Ecole d'lngenieurs du Pas-de-Calais, Campus de la Malassise, B.P. 39, 62967 Longuenesse Cedex, France

Stephane Lecoeuche
M.E.T.I.E.R., Ecole d'lngenieurs du Pas-de-Calais, Campus de la Malassise, B.P. 39, 62967 Longuenesse Cedex, France


This paper focuses on the feasibility of online identification of thermal systems. The transfer function is not looked for, but a black box model is obtained. In the first part, the principles of online identification are reminded. This leads to the definition of the regression vector and of the regressors. Then these principles are applied to neural based techniques which are adapted from standard ARX (AutoRegressive structure with eXtra inputs) and OE (Output-Error) models. For the Neural Network ARX (NNARX) model, only one example is given, which leads to a not fully satisfactory identification. This identification is based on the response of the system to random heat rates during random times. The validation is based on the response to another set of random heat rates and on the response of the system to a step function. For Neural Network OE (NNOE) model, the influence of the number of regressors is presented along with the influence of the number of neurons on the hidden layer. It is shown that many architectures lead to a good identification, but that some particular models may lead to a very poor result. To make the comparison possible between the proposed models, a distance criterion is computed. This leads to the choice of the best adapted architecture.

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