An interval fuzzy number-based fuzzy collaborative forecasting approach for DRAM yield forecasting

Most existing fuzzy collaborative forecasting (FCF) methods adopt type-1 fuzzy numbers to represent fuzzy forecasts. FCF methods based on interval-valued fuzzy numbers (IFNs) are not widely used. However, the inner and outer sections of an IFN-based fuzzy forecast provide meaning information that se...

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Veröffentlicht in:Complex & intelligent systems Jg. 7; H. 1; S. 111 - 122
Hauptverfasser: Chen, Toly, Chiu, Min-Chi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cham Springer International Publishing 01.02.2021
Springer Nature B.V
Schlagworte:
Collaboration
Complexity
Computational Intelligence
Data Structures and Information Theory
Dynamic random access memory
Engineering
Forecasting
Fuzzy systems
Original Article
Random access memory
Mixed binary nonlinear programming
Fuzzy collaborative forecasting
Interval fuzzy number
ISSN:2199-4536, 2198-6053
Online-Zugang:Volltext
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Zusammenfassung:Most existing fuzzy collaborative forecasting (FCF) methods adopt type-1 fuzzy numbers to represent fuzzy forecasts. FCF methods based on interval-valued fuzzy numbers (IFNs) are not widely used. However, the inner and outer sections of an IFN-based fuzzy forecast provide meaning information that serves different managerial purposes, which is a desirable feature for a FCF method. This study proposed an IFN-based FCF approach. Unlike existing IFN-based fuzzy association rules or fuzzy inference systems, the IFN-based FCF approach ensures that all actual values fall within the corresponding fuzzy forecasts. In addition, the IFN-based FCF approach optimizes the forecasting precision and accuracy with the outer and inner sections of the aggregation result, respectively. Based on the experimental results, the proposed FCF-II approach surpassed existing methods in forecasting the yield of a dynamic random access memory product.
Bibliographie:ObjectType-Article-1
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ISSN:2199-4536
2198-6053
DOI:10.1007/s40747-020-00179-8