A. Kumoluyi et al., WELL RESERVOIR MODEL IDENTIFICATION USING TRANSLATION AND SCALE-INVARIANT HIGHER-ORDER NETWORKS, NEURAL COMPUTING & APPLICATIONS, 3(3), 1995, pp. 128-138
Citations number
17
Categorie Soggetti
Computer Sciences, Special Topics","Computer Science Artificial Intelligence
This paper describes the use of higher order neural networks to identi
fy well reservoir response models from test data. Well reservoir respo
nse models are characterised by a family of parametrically related cur
ves. Neural networks can in principle offer an interesting approach to
the identification problem as data are often uncertain and incomplete
. However, it turns out that the well reservoir model, viewed as a cur
ve in two dimensions, is invariant with respect to translation and cha
nges of scale of the axes. This poses severe problems for a standard b
ackpropagation network using the two-dimensional plot as an input reti
na. This difficulty can be overcome by using a higher order network in
which the output is forced to be invariant with respect to the requir
ed transformations of the retina. In this way, the potentially huge nu
mber of weights is significantly reduced using the in Variance conditi
on as a constraint which acts so as to divide the weights into equival
ence classes within which they ave equal. The resulting network can. t
hen be trained using standard techniques. We contrast this network app
roach with classical methods of model identification.