نتایج جستجو برای: fuzzy time series model

تعداد نتایج: 3870057  

Journal: :Journal of Intelligent and Fuzzy Systems 2015
Liping Huang Yongjian Yang Chunsheng Cui

The cross-layered concept of wireless network breaks the traditional network layered concept to realize interaction between layers. In addition, the broadcasting nature of a wireless environment makes cooperative communications possible. In this paper, by utilizing the cross-layer optimization scheme, we introduce the fuzzy time series predication model that has linear computational complexity ...

2011
MEMMEDAGA MEMMEDLI OZER OZDEMIR

Fuzzy approach and artificial neural networks become effective tool for researchers by forecasting fuzzy time series. The relation of these has advantage to improve forecasting performance especially in handling nonlinear systems. Hence, in this study we aimed to handle a nonlinear problem to apply neural network-based fuzzy time series model. Differing from previous studies, we used various de...

Reliability is the most important performance issue in the engineering design process but in the real world problems, there are limitations for using the conventional reliability. Fuzzy logic has proved to be effective in expressing uncertainties in different fields, including reliability engineering. In this paper, For both the series and parallel systems composed of three identical or differe...

2015
Mu-Yen Chen

A novel high-order fuzzy time series model for stock price forecasting is presented based on the fuzzy cmeans (FCM) discretization method and artificial neural networks (ANN). In the proposed model, the FCM discretization method obtained reliable interval lengths. In addition, the fuzzy relation matrix was obtained from ANN, mooting the need for complex and time-consuming matrix operations. The...

Abstract In this paper, a novel technique based on fuzzy method is presented for chaotic nonlinear time series prediction. Fuzzy approach with the gradient learning algorithm and methods constitutes the main components of this method. This learning process in this method is similar to conventional gradient descent learning process, except that the input patterns and parameters are stored in mem...

2009
Alejandro Veloz Héctor Allende-Cid Héctor Allende Claudio Moraga Rodrigo Salas

The aim of this paper is to simultaneously identify and estimate a non-linear autoregressive time series using a flexible neuro-fuzzy model. We provide a self organization and incremental mechanism to the adaptation process of the neuro-fuzzy model. The self organization mechanism searches for a suitable set of premises and consequents to enhance the time series estimation performance, while th...

Ahmad Kalhor, Babak N. Araabi Caro Lucasi

Amongst possible choices for identifying complicated processes for prediction, simulation, and approximation applications, high-order Takagi-Sugeno (TS) fuzzy models are fitting tools. Although they can construct models with rather high complexity, they are not as interpretable as first-order TS fuzzy models. In this paper, we first propose to use Deformed Linear Models (DLMs) in consequence pa...

2004
Melike Sah Konstantin Y. Degtiarev

This paper proposes a novel improvement of forecasting approach based on using time-invariant fuzzy time series. In contrast to traditional forecasting methods, fuzzy time series can be also applied to problems, in which historical data are linguistic values. It is shown that proposed time-invariant method improves the performance of forecasting process. Further, the effect of using different n...

Journal: :Mathematics and Computers in Simulation 2010
Çagdas Hakan Aladag Ufuk Yolcu Erol Egrioglu

Many fuzzy time series approaches have been proposed in recent years. These methods include three main phases such as fuzzification, defining fuzzy relationships and, defuzzification. Aladag et al. [2] improved the forecasting accuracy by utilizing feed forward neural networks to determine fuzzy relationships in high order fuzzy time series. Another study for increasing forecasting accuracy was...

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