RT Journal Article
SR 00
ID 10.1016/S0305-0548(96)00021-4
A1 Stam, A.
A1 Sun, M.
A1 Haines, M.
T1 Artificial neural network representations for hierarchical preference structures
JF Computers & Operations Research
YR 1996
FD 1996-12
VO 23
IS 12
SP 1191
OP 1201
AB In this paper, we introduce two artificial neural network formulations that can be used to assess the preference ratings from the pairwise comparison matrices of the Analytic Hierarchy Process. First, we introduce a modified Hopfield network that can determine the vector of preference ratings associated with a positive reciprocal comparison matrix. The dynamics of this network are mathematically equivalent to the power method, a widely used numerical method for computing the principal eigenvectors of square matrices. However, this Hopfield network representation is incapable of generalizing the preference patterns, and consequently is not suitable for approximating the preference ratings if the pairwise comparison judgments are imprecise. Second, we present a feed-forward neural network formulation that does have the ability to accurately approximate the preference ratings. We use a simulation experiment to verify the robustness of the feed-forward neural network formulation with respect to imprecise pairwise judgments. From the results of this experiment, we conclude that the feed-forward neural network formulation appears to be a powerful tool for analyzing discrete alternative multicriteria decision problems with imprecise or fuzzy ratio-scale preference judgments.
PB Elsevier
SN 0305-0548
LK https://pure.iiasa.ac.at/id/eprint/4602/