Journal of Intelligent & Fuzzy Systems - Volume 23, issue 1

Authors: Davvaz, B. | Corsini, P.

Article Type: Research Article

Abstract: This paper considers a relationship between fuzzy sets and (m,n)-ary hypermodules. We introduce the notion of a fuzzy and anti fuzzy (m,n)-ary sub-hypermodules. A generalization of ideas presented by Davvaz and Corsini [Information Sciences 177 (2007) 865-875] and Bhakat and Das [Fuzzy Sets and Systems 80 (1996) 359-368], is presented. In fact, we study (∈, ∈ ∨ q)-fuzzy (m,n)-ary sub-hypermodules. Also, the concept of a fuzzy (m,n)-ary sub-hypermodule with thresholds is investigated.

Keywords: Hypergroup, hyperring, hypermodule, fuzzy set, fuzzy sub-hypermodule

DOI: 10.3233/IFS-2012-0489

Citation: Journal of Intelligent & Fuzzy Systems, vol. 23, no. 1, pp. 1-8, 2012

Authors: Davvaz, B. | Hassani Sadrabadi, E. | Leoreanu-Fotea, V.

Article Type: Research Article

Abstract: The length of the sequence of join spaces and Atanassov's intuitionistic fuzzy sets associated with a hypergroupoid H is called the Atanassov's intuitionistic fuzzy grade of H. In this paper, we consider a certain sequence of Atanassov's intuitionistic fuzzy sets and join spaces determined by a hypergroupoid associated with a hypergraph. We study this sequence in several particular cases and determine the Atanassov's intuitionistic fuzzy grade of a such sequence.

Keywords: Fuzzy set, Atanassov's intuitionistic fuzzy set, hypergraph, fuzzy grade, join space

DOI: 10.3233/IFS-2012-0490

Citation: Journal of Intelligent & Fuzzy Systems, vol. 23, no. 1, pp. 9-25, 2012

Authors: Luna, Ivette | Ballini, Rosangela

Article Type: Research Article

Abstract: This paper introduces an adaptive fuzzy rule-based system applied as a financial time series model for volatility forecasting. The model is based on Takagi–Sugeno fuzzy systems and is built in two phases: In the first, the model uses the subtractive clustering algorithm to determine initial group structures in a reduced data set. In the second phase, the system is modified dynamically by adding and pruning operators and applying a recursive learning algorithm based on the expectation maximization optimization technique. The algorithm automatically determines the number of fuzzy rules necessary at each step, and one-step-ahead predictions are estimated and parameters updated. …The model is applied to forecast financial time series volatility, considering daily values of the São Paulo stock exchange index, the Petrobras preferred stock prices, and the BRL/USD exchange rate. The model suggested is compared against generalized autoregressive conditional heteroskedasticity models. Experimental results show the adequacy of the adaptive fuzzy approach for volatility forecasting purposes. Show more

Keywords: Adaptive fuzzy systems, online learning, time series forecasting, volatility, GARCH

DOI: 10.3233/IFS-2012-0491

Citation: Journal of Intelligent & Fuzzy Systems, vol. 23, no. 1, pp. 27-38, 2012

Article Type: Book Review

DOI: 10.3233/IFS-2012-0492

Citation: Journal of Intelligent & Fuzzy Systems, vol. 23, no. 1, pp. 39-40, 2012