NDLI: Cost prediction method based on an improved fuzzy model

Content Provider	Springer Nature Link
Author	Zhai, Kun Jiang, Nan Pedrycz, Witold
Copyright Year	2012
Abstract	Scientific cost prediction is the foundation for formulating a business plan, which helps enterprises to determine the target costs and to control the operating costs. In today’s world, quantitative methods are crucial for prediction purposes in cost management. On the basis of studying some typical methods of cost prediction, we propose a cost prediction method based on an improved fuzzy model. Firstly, owning to the inefficiency of existing fuzzifier, such that it is difficult to determine the number of fuzzy rules, we present an improved fuzzy c-means (FCM) fuzzifier to solve this problem referring to “granulation–degranulation” strategy and sample error function. Secondly, a fuzzy model based on the improved FCM fuzzifier is constructed through combining Takagi–Sugen–Kang fuzzy inference system. The algorithm of the improved fuzzy model is also realized by this study. Finally, through a series of experimental analyses for steel number product and steel class product of certain machinery enterprise, the results have demonstrated that the proposed method has the better prediction accuracy, which can be used effectively and practically in real-world cost prediction.
Starting Page	1045
Ending Page	1053
Page Count	9
File Format	PDF
ISSN	02683768
Journal	The International Journal of Advanced Manufacturing Technology
Volume Number	65
Issue Number	5-8
e-ISSN	14333015
Language	English
Publisher	Springer-Verlag
Publisher Date	2012-05-26
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Cost prediction Artificial intelligence Fuzzy model FCM fuzzifier Time series Machinery enterprises Industrial and Production Engineering Production/Logistics/Supply Chain Mechanical Engineering Computer-Aided Engineering (CAD, CAE) and Design
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Control and Systems Engineering Mechanical Engineering Computer Science Applications Software

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