Mashinchi, Reza and Selamat, Ali Thanh and Ibrahim, Suhaimi and Krejcar, Ondrej and Penhaker, Marek (2015) Evaluating customer satisfaction: linguistic reasoning by fuzzy artificial neural networks. Studies in Computational Intelligence, 598 . pp. 91-100. ISSN 1860-949X
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Official URL: http://dx.doi.org/10.1007/978-3-319-16211-9_10
Abstract
Customer satisfaction is a measure of how a company meets or surpasses customers' expectations. It is seen as a key element in business strategy; and therefore, enhancing the methods to evaluate the satisfactory level is worth studying. Collecting rich data to know the customers’ opinion is often encapsulated in verbal forms, or linguistic terms, which requires proper approaches to process them. This paper proposes and investigates the application of fuzzy artificial neural networks (FANNs) to evaluate the level of customer satisfaction. Genetic algorithm (GA) and back-propagation algorithm (BP) adjust the fuzzy variables of FANN. To investigate the performances of GA- and BP-based FANNs, we compare the results of each algorithm in terms of obtained error on each alpha-cut of fuzzy values
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | fuzzy artificial neural networks, genetic algorithms, prediction, rich data |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Advanced Informatics School |
| ID Code: | 55070 |
| Deposited By: | Fazli Masari |
| Deposited On: | 09 Aug 2016 04:49 |
| Last Modified: | 15 Feb 2017 07:33 |
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