Fuzzy time series and its models
Fuzzy Sets and Systems
Forecasting enrollments with fuzzy time series—part I
Fuzzy Sets and Systems
Forecasting enrollments with fuzzy time series—part II
Fuzzy Sets and Systems
Forecasting enrollments based on fuzzy time series
Fuzzy Sets and Systems
Handling forecasting problems using fuzzy time series
Fuzzy Sets and Systems
The development of fuzzy decision trees in the framework of Axiomatic Fuzzy Set logic
Applied Soft Computing
Information Sciences: an International Journal
Information Sciences: an International Journal
Adaptive fuzzy control for a class of uncertain nonaffine nonlinear systems
Information Sciences: an International Journal
Forecasting in high order fuzzy times series by using neural networks to define fuzzy relations
Expert Systems with Applications: An International Journal
Observer-based fuzzy adaptive control for strict-feedback nonlinear systems
Fuzzy Sets and Systems
A combined backstepping and small-gain approach to robust adaptive fuzzy output feedback control
IEEE Transactions on Fuzzy Systems
Trend-weighted fuzzy time-series model for TAIEX forecasting
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Duel fuzzy logic controllers design for autonomous robot navigation
International Journal of Systems, Control and Communications
The fuzzy clustering analysis based on AFS theory
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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A number of methods have been proposed for forecasting based on fuzzy time series. Most of fuzzy time series are presented for forecasting enrolments. In this paper, we propose an innovate fuzzy time series forecasting model using axiomatic fuzzy set (AFS) theory. The advantages of using AFS theory in this approach are multiple: fuzzy sets with more than one maximum value are obtained, this affects considerably the forecasting accuracy; values of membership degrees are directly obtained from the data. Compared with existing methods, the experimental study shows that the proposed method can get best forecasting accuracy rate over methods described in the literature.