@inbook{a9e16879404e4ae5a1f0caa38825c72f,
title = "Neuro-fuzzy approximation of multi-criteria decision-making QFD methodology",
abstract = "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.",
keywords = "AHP, ANFIS, Decision-making, Fuzziness patterns, Level-of-satisfaction, QFD",
author = "Ajith Abraham and Pandian Vasant and Arijit Bhattacharya",
year = "2008",
month = jan,
day = "1",
doi = "10.1007/978-0-387-76813-7_12",
language = "English",
isbn = "978-0-387-76812-0",
series = "Springer Optimization and Its Applications",
publisher = "Springer",
pages = "301--321",
booktitle = "Springer Optimization and Its Applications",
address = "Germany",
}