%A A. Abraham
%A P. Vasant
%A A. Bhattacharya
%D 2008
%L scholars488
%O cited By 9
%X 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. © Springer Science + Business Media, LLC 2008.
%T Neuro-fuzzy approximation of multi-criteria decision-making QFD methodology
%J Springer Optimization and Its Applications
%R 10.1007/978-0-387-76813-7₁₂
%V 16
%I Springer International Publishing
%P 301-321