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Applied Computational Intelligence and Soft Computing
Volume 2012 (2012), Article ID 402420, 16 pages
http://dx.doi.org/10.1155/2012/402420
Research Article

Estimation of Fuzzy Measures Using Covariance Matrices in Gaussian Mixtures

Department of Electrical Engineering, Indian Institute of Technology Kanpur, Kanpur 208016, India

Received 5 May 2011; Revised 7 February 2012; Accepted 9 February 2012

Academic Editor: Enric Trillas

Copyright © 2012 Nishchal K. Verma. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper presents a novel computational approach for estimating fuzzy measures directly from Gaussian mixtures model (GMM). The mixture components of GMM provide the membership functions for the input-output fuzzy sets. By treating consequent part as a function of fuzzy measures, we derived its coefficients from the covariance matrices found directly from GMM and the defuzzified output constructed from both the premise and consequent parts of the nonadditive fuzzy rules that takes the form of Choquet integral. The computational burden involved with the solution of λ-measure is minimized using Q-measure. The fuzzy model whose fuzzy measures were computed using covariance matrices found in GMM has been successfully applied on two benchmark problems and one real-time electric load data of Indian utility. The performance of the resulting model for many experimental studies including the above-mentioned application is found to be better and comparable to recent available fuzzy models. The main contribution of this paper is the estimation of fuzzy measures efficiently and directly from covariance matrices found in GMM, avoiding the computational burden greatly while learning them iteratively and solving polynomial equations of order of the number of input-output variables.