Abstract
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.
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Gomez-Ruiz, J.A., Karanik, M., Peláez, J.I. (2009). Improving the Consistency of AHP Matrices Using a Multi-layer Perceptron-Based Model. In: Cabestany, J., Sandoval, F., Prieto, A., Corchado, J.M. (eds) Bio-Inspired Systems: Computational and Ambient Intelligence. IWANN 2009. Lecture Notes in Computer Science, vol 5517. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02478-8_6
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DOI: https://doi.org/10.1007/978-3-642-02478-8_6
Publisher Name: Springer, Berlin, Heidelberg
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