International Journal of Fuzzy Systems (INT J FUZZY SYST )

Description

  • Impact factor
    1.51
    Show impact factor history
     
    Impact factor
  • 5-year impact
    1.19
  • Cited half-life
    3.30
  • Immediacy index
    0.25
  • Eigenfactor
    0.00
  • Article influence
    0.19
  • ISSN
    1562-2479

Publications in this journal

  • [Show abstract] [Hide abstract]
    ABSTRACT: To support investment decision based on technical analysis (TA), this study aims to retrieve the knowledge or rules of various indicators by a hybrid soft computing model. Though the validity of TA has been examined extensively by various statistical methods in literature, previous studies mainly explored the effectiveness of each technical indicator separately; therefore, a practical approach that may consider the inconsistency of various indicators simultaneously and control the down-side risk of an investment decision is still under-explored. Thus, a hybrid model—by constructing a variable consistency dominance-based rough set approach (VC-DRSA) information system with the fuzzy inference enhanced discretization of signals—is proposed, to retrieve the imprecise patterns from commonly adopted technical indicators. At the first stage, the trading signals (i.e., Buy, Neutral, or Sell) are pre-processed in two groups: straightforward and complicated signals. The straight-forward technical indicators (i.e., for signals that are decided by precise rules) are suggested by domain experts, and the buy-in signals are simulated by several trading strategies to examine the outcomes of each indicator. As for the complicated group (i.e., for signals that require imprecise judgments to make a decision), a fuzzy inference technique is incorporated to enhance the discretization of signals; those signals are also simulated by the aforementioned trading strategies to obtain the corresponding results. At the second stage, the trading signals generated by each technical indicator and their pertinent results from the previous stage are combined for VC-DRSA modeling to gain decision rules. To illustrate the proposed model, the weighted average index of the Taiwan stock market was examined from mid/2002 to mid/2014, and a set of decision rules with nearly 80% classification accuracy (both in the training and the testing sets) were obtained in this empirical case. The findings suggest that several technical indicators should be considered simultaneously, and the retrieved rules (knowledge) have practical implications for investors.
    International Journal of Fuzzy Systems 03/2015;
  • [Show abstract] [Hide abstract]
    ABSTRACT: To support investment decision based on technical analysis (TA), this study aims to retrieve the knowledge or rules of various indicators by a hybrid soft computing model. Though the validity of TA has been examined extensively by various statistical methods in literature, previous studies mainly explored the effectiveness of each technical indicator separately; therefore, a practical approach that may consider the inconsistency of various indicators simultaneously and control the down-side risk of an investment decision is still under-explored. Thus, a hybrid model—by constructing a variable consistency dominance-based rough set approach (VC-DRSA) information system with the fuzzy inference enhanced discretization of signals—is proposed, to retrieve the imprecise patterns from commonly adopted technical indicators. At the first stage, the trading signals (i.e., Buy, Neutral, or Sell) are pre-processed in two groups: straightforward and complicated signals. The straight-forward technical indicators (i.e., for signals that are decided by precise rules) are suggested by domain experts, and the buy-in signals are simulated by several trading strategies to examine the outcomes of each indicator. As for the complicated group (i.e., for signals that require imprecise judgments to make a decision), a fuzzy inference technique is incorporated to enhance the discretization of signals; those signals are also simulated by the aforementioned trading strategies to obtain the corresponding results. At the second stage, the trading signals generated by each technical indicator and their pertinent results from the previous stage are combined for VC-DRSA modeling to gain decision rules. To illustrate the proposed model, the weighted average index of the Taiwan stock market was examined from mid/2002 to mid/2014, and a set of decision rules with nearly 80% classification accuracy (both in the training and the testing sets) were obtained in this empirical case. The findings suggest that several technical indicators should be considered simultaneously, and the retrieved rules (knowledge) have practical implications for investors.
    International Journal of Fuzzy Systems 03/2015;
  • International Journal of Fuzzy Systems 09/2014; Vol. 16, No. 4.
  • International Journal of Fuzzy Systems 01/2014; 16(2):160-172.
  • [Show abstract] [Hide abstract]
    ABSTRACT: We generalize the power averaging operators to interval-valued Atanassov’s intuitionistic fuzzy environments, and develop a series of generalized interval-valued Atanassov’s intuitionistic fuzzy power aggregation operators. The main advantages of these operators are that they not only accommodate situations in which the input arguments are interval-valued intuitionistic fuzzy numbers (IVIFNs), but also consider information about the relationship between the IVIFNs being fused. The properties of these operators are investigated and the relationships among these operators are discussed. Moreover, approaches to multiple attributes group decision making based on the proposed operators are given and two examples are illustrated to show the feasibility and validity of the new approaches to the application of multiple attributes group decision making.
    International Journal of Fuzzy Systems 12/2013; 15(4):401-411.
  • International Journal of Fuzzy Systems 12/2013; 15(4):460-470.
  • International Journal of Fuzzy Systems 03/2013; 15(1):9-21.
  • [Show abstract] [Hide abstract]
    ABSTRACT: The control criticality class is probably the first feature that will be pronounced by the spare parts (SPs) logistics practitioners, but it has been found that no systematic and well-structured procedures exist to evaluate the control criticality. This paper presents a systematic and scientific approach to identify the criticality classes of SPs under highly uncertain environment with limited data and information. By using group-discussing and anonymous questionnaire methods, the criteria to evaluate criticality classes of SPs are proposed. A practical and reliable algorithm integrated of AHP, fuzzy comprehensive evaluation and grey relational analysis (GRA) is utilized to convert the qualitative description to quantitative data. Subsequently, its effectiveness and superiority are tested by a practical example. Moreover, case studies are rare for the evaluation of SPs control criticality in large power plants. We also conducted a case study to test the proposed model. Empirical findings suggest that the proposed model is successful in correcting unreasonable criticality classes setting of SPs that are important to inventory control in a famous power plant in China.
    International Journal of Fuzzy Systems 09/2012; 14(3):392-401.