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    A stepwise based fuzzy regression procedure for developing customer preference models in new product development

    229698_229698.pdf (370.3Kb)
    Access Status
    Open access
    Authors
    Chan, Kit Yan
    Lam, H.K.
    Dillon, T.
    Ling, S.
    Date
    2015
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Chan, K.Y. and Lam, H.K. and Dillon, T. and Ling, S. 2015. A stepwise based fuzzy regression procedure for developing customer preference models in new product development. IEEE Transactions on Fuzzy Systems. 23 (5): pp. 1728-1745.
    Source Title
    IEEE Transactions on Fuzzy Systems
    DOI
    10.1109/TFUZZ.2014.2375911
    ISSN
    10636706
    School
    Department of Electrical and Computer Engineering
    Remarks

    Copyright © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

    URI
    http://hdl.handle.net/20.500.11937/45723
    Collection
    • Curtin Research Publications
    Abstract

    Fuzzy regression methods have commonly been used to develop consumer preferences models which correlate the engineering characteristics with consumer preferences regarding a new product; the consumer preference models provide a platform whereby product developers can decide the engineering characteristics in order to satisfy consumer preferences prior to developing the products. Recent research shows that these fuzzy regression methods are commonly used to model customer preferences. However, these approaches have a common limitation in that they do not investigate the appropriate polynomial structure which includes significant regressors with only significant engineering characteristics; also, they cannot generate interaction or high-order regressors in the models. The inclusion of insignificant regressors is not an effective approach when developing the models. Exclusion of significant regressors may affect the generalization capability of the consumer preference models. In this paper, a novel fuzzy modelling method is proposed, namely fuzzy stepwise regression (F-SR), in order to develop a customer preference model which is structured with an appropriate polynomial which includes only significant regressors.Based on the appropriate polynomial structure, the fuzzy coefficients are determined using the fuzzy least square regression. The developed fuzzy regression model attempts to obtain a better generalization capability using a smaller number of regressors. The effectiveness of the F-SR is evaluated based on two design problems, namely a tea maker design and a solder paste dispenser design. Results show that better generalization capabilities can be obtained compared with the fuzzy regression methods commonly-used for new product development. Also, smaller-scale consumer preference models with fewer engineering characteristics can be obtained. Hence, a simpler and more effective product development platform can be provided.

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