Neuro-fuzzy approximation of multi-criteria decision-making QFD methodology

Abraham, Ajith, Vasant, Pandian and Bhattacharya, Arijit ORCID: (2008) Neuro-fuzzy approximation of multi-criteria decision-making QFD methodology. In: Springer Optimization and Its Applications. Springer Optimization and Its Applications . Springer, pp. 301-321. ISBN 978-0-387-76812-0

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This chapter demonstrates how a neuro-fuzzy approach could produce outputs of a further-modified multi-criteria decision-making (MCDM) quality function deployment (QFD) model within the required error rate. The improved fuzzified MCDM model uses the modified S-curve membership function (MF) as stated in an earlier chapter. The smooth and flexible logistic membership function (MF) finds out fuzziness patterns in disparate level-of-satisfaction for the integrated analytic hierarchy process (AHP-QFD model. The key objective of this chapter is to guide decision makers in finding out the best candidate-alternative robot with a higher degree of satisfaction and with a lesser degree of fuzziness.

Item Type: Book Section
Uncontrolled Keywords: ahp,anfis,decision-making,fuzziness patterns,level-of-satisfaction,qfd,control and optimization ,/dk/atira/pure/subjectarea/asjc/2600/2606
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Depositing User: LivePure Connector
Date Deposited: 05 May 2020 00:12
Last Modified: 22 Oct 2022 23:51
DOI: 10.1007/978-0-387-76813-7_12

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