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Journal Cover International Journal of Fuzzy Computation and Modelling
  [2 followers]  Follow
    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 2052-353X - ISSN (Online) 2052-3548
   Published by Inderscience Publishers Homepage  [416 journals]
  • Construction of a fuzzy probability space with Gumbel function, Gaussian
           function, derivative of Gaussian function and Weibull function
    • Authors: Rituparna Chutia, D. Datta
      Pages: 1 - 13
      Abstract: Random variable basically addresses a probability space and fuzzy random variable (FRV) will address the fuzzy probability space. Concepts of FRV valued functions such as exponential function, logarithmic function and power function have been already researched. But applications in the field of failure analysis of structures very often are dealt with extreme value probability distribution functions such as Gumbel, Frechet (Type-I and Type II) and Weibull function. However such functions are well defined in presence of a large number of data. But the failure analysis of structures with insufficient information in the similar footing needs corresponding FRV valued functions. Therefore the basic thrust of this paper is to propose a concept of formulating FRV valued such type of extreme value distribution functions viz., Gumbel, Frechet and the Weibull. In this paper we have proposed the FRV valued Gumbel and Weibull function. In addition to this we have also proposed the similar concept for FRV valued Gaussian and its derivative function. Fundamental properties of these functions in the fuzzy probability space are also discussed in this paper.
      Keywords: failure analysis; fuzzy probability space; Gumbel function; Gaussian function; Weibull function; fuzzy random variables; FRVs; structural failure
      Citation: International Journal of Fuzzy Computation and Modelling, Vol. 2, No. 1 (2016) pp. 1 - 13
      PubDate: 2016-07-20T23:20:50-05:00
      DOI: 10.1504/IJFCM.2016.077871
      Issue No: Vol. 2, No. 1 (2016)
       
  • Interactive intuitionistic fuzzy technique in multi-objective
           optimisation
    • Authors: Arindam Garai, Palash Mandal, Tapan Kumar Roy
      Pages: 14 - 26
      Abstract: In this paper, an interactive approach in multi objective optimisation problem under intuitionistic fuzzy environment is presented. This intuitionistic fuzzy problem is first converted into a single objective crisp optimisation problem. In proposed approach, in each step, decision maker (DM) is requested to update the reference acceptance level and reference rejection level of referred objective only. Membership and non-membership grades of all other objectives are automatically updated from the list of trade off rates in each stage. It is repeated until DM is satisfied with the optimal solution. An illustrative numerical example is provided to demonstrate the feasibility and efficiency of the proposed method and conclusions are drawn.
      Keywords: interactive fuzzy optimisation; intuitionistic fuzzy sets; decision making; uncertainty; intuitionistic fuzzy optimisation; multi-objective optimisation; trade off rates; Pareto optimal solution
      Citation: International Journal of Fuzzy Computation and Modelling, Vol. 2, No. 1 (2016) pp. 14 - 26
      PubDate: 2016-07-20T23:20:50-05:00
      DOI: 10.1504/IJFCM.2016.077872
      Issue No: Vol. 2, No. 1 (2016)
       
  • Fuzzy C-means load frequency controller in deregulated power
           environment
    • Authors: S. Srikanth, K.R. Sudha, Y. Butchi Raju
      Pages: 27 - 49
      Abstract: The load frequency control (LFC) problem has been a major subject in the power system design/operation. The evolution of many socialised companies for power generation affects the formulation of LFC problem. In the present paper, a fuzzy load frequency controller with minimum rule-base is proposed. The optimal rule base for the proposed controller is obtained from fuzzy C-means clustering technique. The efficacy of the proposed fuzzy C-means load frequency controller is verified and compared with existing techniques in the literature for a three area interconnected power system in deregulated environment for various operating conditions under different nonlinearities. The proposed controller is tested for practical generation plants (NTTPS, KTPS, and RTPS) in India.
      Keywords: load frequency control; LFC; PID controllers; fuzzy control; fuzzy C-means clustering; deregulated power systems; India
      Citation: International Journal of Fuzzy Computation and Modelling, Vol. 2, No. 1 (2016) pp. 27 - 49
      PubDate: 2016-07-20T23:20:50-05:00
      DOI: 10.1504/IJFCM.2016.077874
      Issue No: Vol. 2, No. 1 (2016)
       
  • Set of non-dominated fuzzy subsets and fuzzy set of non-dominated
           vertices in fuzzy graphs
    • Authors: A. Ibrahim, Basheer Ahamed Mohideen
      Pages: 50 - 60
      Abstract: In this paper, the set of non-dominated vertices of crisp graphs is discussed. We consider the two logical operators, and a composition. We introduce the set of non-dominated fuzzy subsets NDFS and fuzzy set of non-dominated vertices of fuzzy graphs FND. Also, we obtain some properties of NDFS and FND.
      Keywords: logical operators; fuzzy graphs; non-dominated fuzzy subsets; non-dominated vertices; weak lattice; sub-weak lattice; fuzzy sets
      Citation: International Journal of Fuzzy Computation and Modelling, Vol. 2, No. 1 (2016) pp. 50 - 60
      PubDate: 2016-07-20T23:20:50-05:00
      DOI: 10.1504/IJFCM.2016.077875
      Issue No: Vol. 2, No. 1 (2016)
       
  • A fuzzy logic model to forecast momentum in stock markets
    • Authors: G.P. Pandey, Sanjay Sharma
      Pages: 61 - 75
      Abstract: Trend identification is a visual process where we can draw and see the trend line, then suggest the trend. But to make the system understand this trend is very tough. Using fuzzy logic first we try to make the system understand the actual trend and verify with what we can see and then we go on for forecasting the future trend. Fuzzy membership functions are the key elements while creating any fuzzy system. For generating these membership functions, usually two sources are used, i.e., expert knowledge and real time data. Expert knowledge may not be available all the time, but the probability of getting real time data is more. Here we have tried to develop a method by which fuzzification of real time data can be done and then identification of the trend can be done using those fuzzy values after which forecasting of the short term trend can be done. The type of real time data used here is the daily values of NIFTY 50 index used in National Stock Exchange of India for stock futures trading.
      Keywords: forecasting; fuzzy logic; NIFTY 50; stock markets; stock market momentum; short term trends; futures trading; India.
      Citation: International Journal of Fuzzy Computation and Modelling, Vol. 2, No. 1 (2016) pp. 61 - 75
      PubDate: 2016-07-20T23:20:50-05:00
      DOI: 10.1504/IJFCM.2016.077881
      Issue No: Vol. 2, No. 1 (2016)
       
  • New decomposition method for solving dual fully fuzzy linear systems
    • Authors: G.P. Pandey, Sanjay Sharma
      Pages: 76 - 85
      Abstract: Various methods are proposed by different authors to solve linear equation for dual fuzzy system. Also, in the dual fully fuzzy linear system (DFFLS) all parameters are considered to be fuzzy numbers. In this manuscript, we have extended symmetric and triangular (ST) decomposition to solve the DFFLS. Since triangular fuzzy numbers is a special case of trapezoidal fuzzy numbers, then on solving the DFFLS with trapezoidal fuzzy numbers is discussed. The ST decomposition method can solve these systems in a smaller computing process. The explicit scheme is given and then the proposed algorithm is illustrated with solving some numerical examples.
      Keywords: dual fuzzy systems; linear equations; dual fully fuzzy linear systems; DFFLS; trapezoidal fuzzy numbers; symmetric decomposition; triangular decomposition
      Citation: International Journal of Fuzzy Computation and Modelling, Vol. 2, No. 1 (2016) pp. 76 - 85
      PubDate: 2016-07-20T23:20:50-05:00
      DOI: 10.1504/IJFCM.2016.077883
      Issue No: Vol. 2, No. 1 (2016)
       
 
 
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