Journal ArticleDOI
Fuzzy grey GM(1, 1) model under fuzzy system
TLDR
The fuzzy system derived from collected data is considered by the fuzzy grey controlled variable to derive a fuzzy grey GM(1, 1) model to forecast the extrapolative values under the fuzzy system.Abstract:
Grey GM(1, 1) forecasting model is a kind of short-term forecasting method which has been successfully applied in management and engineering problems with as little as four data. However, when a new system is constructed, the system is uncertain and variable so that the collected data is usually of fuzzy type, which could not be applied to grey GM(1, 1) model forecast. In order to cope with such problem, the fuzzy system derived from collected data is considered by the fuzzy grey controlled variable to derive a fuzzy grey GM(1, 1) model to forecast the extrapolative values under the fuzzy system. Finally, an example is described for illustration.read more
Citations
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Book
Fuzzy sets and systems
TL;DR: Fuzzy sets as mentioned in this paper are a class of classes in which there may be grades of membership intermediate between full membership and non-membership, i.e., a fuzzy set is characterized by a membership function which assigns to each object its grade of membership.
Journal ArticleDOI
Forecasting the output of integrated circuit industry using genetic algorithm based multivariable grey optimization models
TL;DR: A grey model with factor analysis techniques to deal with the multi-factor forecasting problems and the improved multivariable grey forecasting models are found to be feasible and effective.
Journal ArticleDOI
Forecasting agricultural output with an improved grey forecasting model based on the genetic algorithm
TL;DR: In this paper, both modified background value calculation and use of a genetic algorithm to find the optimal parameters were adopted simultaneously to construct an improved GM(1,1) model (GAIGM 1,1)).
Journal ArticleDOI
Multiple stages grey target decision making method with incomplete weight based on multi-granularity linguistic label
Jianjun Zhu,Keith W. Hipel +1 more
TL;DR: Weight models of criteria and of stages, which are based on the requirement of maximum difference of alternatives and restriction of stage harmony, are suggested and can help to avoid faulty decision making due to uncertainty.
Journal ArticleDOI
Supporting Better Decision-Making: A Combined Grey Model and Data Envelopment Analysis for Efficiency Evaluation in E-Commerce Marketplaces
TL;DR: In this paper, a hybrid approach that conducts performance prediction and evaluation of the e-commerce industry by combining the Grey model and data envelopment analysis, i.e., the Malmquist-I-C model, is proposed.
References
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Journal ArticleDOI
Introduction to Grey system theory
TL;DR: The Grey Systeni and its applications are interdisciplinary, cutting across a variety of specialized fields, and it is evident that Grey System theory stands the test of time since 1982 as mentioned in this paper.
Journal ArticleDOI
Fuzzy time series and its models
Qiang Song,Brad S. Chissom +1 more
TL;DR: The definition of fuzzy time series is given, some properties of fuzzyTime series are explored, and procedures to develop fuzzy timeseries models are discussed.
Book
Fuzzy sets and systems
TL;DR: Fuzzy sets as mentioned in this paper are a class of classes in which there may be grades of membership intermediate between full membership and non-membership, i.e., a fuzzy set is characterized by a membership function which assigns to each object its grade of membership.
Journal ArticleDOI
Applications of improved grey prediction model for power demand forecasting
Che-Chiang Hsu,Chia-Yon Chen +1 more
TL;DR: An improved grey GM(1,1) model is proposed, using a technique that combines residual modification with artificial neural network sign estimation, and this method obviously can improve the prediction accuracy of the original grey model.
Journal ArticleDOI
Applying the Grey prediction model to the global integrated circuit industry
TL;DR: In this article, the precision of the Grey forecasting model applied to samples based on demand and sales in the global integrated circuit (IC) industry is examined, and the main objective is to explore which forecast model is most appropriate for the IC industry by comparing the empirical results from the Grey model, time series and exponential smoothing.