Advertisement
Published in:
2009 | OriginalPaper | Chapter
Improving the Consistency of AHP Matrices Using a Multi-layer Perceptron-Based Model
Authors : Jose Antonio Gomez-Ruiz, Marcelo Karanik, José Ignacio Peláez
Published in: Bio-Inspired Systems: Computational and Ambient Intelligence
Publisher: Springer Berlin Heidelberg
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
The Analytic Hierarchy Process (AHP) uses hierarchical structures to arrange comparing criteria and alternatives in order to give support in decision making tasks. The comparisons are realized using pairwise matrices which are filled according to the decision maker criterion. Then, matrix consistency is tested and priorities of alternatives are obtained. If a pairwise matrix is incomplete, two procedures must be realized: first, to complete the matrix with adequate values for missing entries and, second, to improve the consistency matrix to an acceptable level. In this paper a model based on Multi-layer Perceptron (MLP) neural networks is presented. This model is capable of completing missing values in AHP pairwise matrices and improving its consistency at the same time.