全球智库信息集成服务系统(G2TT): Artificial neural network representations for hierarchical preference structures.

G2TT

来源类型	Article
规范类型	其他
DOI	10.1016/S0305-0548(96)00021-4
	Artificial neural network representations for hierarchical preference structures.
	Stam A; Sun M; Haines M
发表日期	1996
出处	Computers & Operations Research 23 (12): 1191-1201
出版年	1996
语种	英语
摘要	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.
主题	Institute Scholars (INS)
URL	http://pure.iiasa.ac.at/id/eprint/4602/
来源智库	International Institute for Applied Systems Analysis (Austria)
引用统计
资源类型	智库出版物
条目标识符	http://119.78.100.153/handle/2XGU8XDN/127462
推荐引用方式 GB/T 7714	Stam A,Sun M,Haines M. Artificial neural network representations for hierarchical preference structures.. 1996.