Computational & Technology Resources
an online resource for computational,
engineering & technology publications 

CivilComp Proceedings
ISSN 17593433 CCP: 64
COMPUTATIONAL ENGINEERING USING METAPHORS FROM NATURE Edited by: B.H.V. Topping
Paper I.9
The Scattered Data Interpolation Procedure based on a Counterpropagation Neural Network S. Lukaszyk
Institute of Computer Methods in Civil Engineering, Crakow University of Technology, Crakow, Poland S. Lukaszyk, "The Scattered Data Interpolation Procedure based on a Counterpropagation Neural Network", in B.H.V. Topping, (Editor), "Computational Engineering using Metaphors from Nature", CivilComp Press, Edinburgh, UK, pp 5963, 2000. doi:10.4203/ccp.64.1.9
Abstract
The Scattered Data Interpolation Procedure (SDP) based on a
modified Counterpropagation Neural Network (CPN) is presented.
Matrix representation of SDIP facilitate the description
of learning and working algorithms in comparison to a
twolayered representation of CPN with competition and interpolation
layers. The data matrix in SDP corresponds to the
competition layer in CPN and the value matrix in SDIP corresponds
to the interpolation layer in CPN. It is shown that during
the working phase SDP, having an input vector X and
output vector Y of size G, maps G scalar functions of vector
argument Y_g = F_g(X), where g = 0,1, ..., G1, rather than one
vector function of vector argument Y = F(X). Two presented
SDP learning algorithms allow compression of information
stored in SDIP matrices during the learning phase, in dependence
of a resolution parameter and thus allowing faster interpolation
during the working phase. The results of a numerical
example show that SDIP algorithms are superior to the
weighted summation function used as an interpolation formula
in CPN. The New Output Compression Algorithm (OCA),
which evaluates the current efficiency of interpolation at each
step of the learning process, is proposed.
purchase the fulltext of this paper (price £20)
go to the previous paper 
