This paper is concerned with the question of continuity of the mapping from
observed time series to models. The behavioral framework is adopted to for
malize a model identification problem in which the observed time series is
decomposed into a part explained by a model and a remaining part which is a
scribed to noise. The misfit between data and model is defined symmetricall
y in the system variables and measured in the.. or amplitude norm. With the
introduction of proper notions of convergence, it is shown that the misfit
function continuously depends on both the data and the model. Two notions
of consistency are formalized and it is shown that the continuity of the mi
sfit function implies a consistent identification of optimal and suboptimal
models. (C) 2001 Elsevier Science B.V. All rights reserved.