Displaying similar documents to “The distribution of mathematical expectations of a randomized fuzzy variable.”

The fuzzy hyperbolic inequality index of fuzzy random variables in finite populations.

Norberto Corral, María Angeles Gil, Hortensia López-García (1996)

Mathware and Soft Computing

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This paper presents an approach to the problem of quantifying the inequality of a finite population with respect to a (social, economical, etc.) fuzzy-valued attribute. For this purpose, the fuzzy hyperbolic inequality index is introduced, and some properties extending the basic ones for real-valued attributes are examined.

A new fuzzy Lyapunov approach to non-quadratic stabilization of Takagi-Sugeno fuzzy models

Ibtissem Abdelmalek, Noureddine Goléa, Mohamed Hadjili (2007)

International Journal of Applied Mathematics and Computer Science

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In this paper, new non-quadratic stability conditions are derived based on the parallel distributed compensation scheme to stabilize Takagi-Sugeno (T-S) fuzzy systems. We use a non-quadratic Lyapunov function as a fuzzy mixture of multiple quadratic Lyapunov functions. The quadratic Lyapunov functions share the same membership functions with the T-S fuzzy model. The stability conditions we propose are less conservative and stabilize also fuzzy systems which do not admit a quadratic stabilization....

Numerical experimentation and comparison of fuzzy integrals.

Manuel Jorge Bolaños, Luis Daniel Hernández, Antonio Salmerón (1996)

Mathware and Soft Computing

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Sugeno and Choquet integrals have been widely studied in the literature from a theoretical viewpoint. However, the behavior of these functionals is known in a general way, but not in practical applications and in particular cases. This paper presents the results of a numerical comparison that attempts to be a basis for a better comprehension and usefulness of both integrals.

Mathematical aspects of the theory of measures of fuzziness.

Doretta Vivona (1996)

Mathware and Soft Computing

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After recalling the axiomatic concept of fuzziness measure, we define some fuzziness measures through Sugeno's and Choquet's integral. In particular, for the so-called homogeneous fuzziness measures we prove two representation theorems by means of the above integrals.

Method of least squares applied to the generalized modus ponens with interval-valued fuzzy sets.

Eduard Agustench, Humberto Bustince, Victoria Mohedano (1999)

Mathware and Soft Computing

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Firstly we present a geometric interpretation of interval-valued fuzzy sets. Secondly, we apply the method of least squares to the fuzzy inference rules when working with these sets. We begin approximating the lower and upper extremes of the membership intervals to axb type functions by means of the method of least squares. Then we analyze a technique for evaluating the conclusion of the generalized modus ponens and we verify the fulfillment of Fukami and alumni axioms [9].

The internal rate of return of fuzzy cash flows.

Loredana Biacino, M. Rosaria Simonelli (1992)

Stochastica

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An internal rate of return (IRR) of an investment or financing project with cash flow (a0,a1,a2,...,an) is usually defined as a rate of interest r such that a0 + a1(1 + r)-1 + ... + an(1 + r)-n = 0. If the cash flow has one sign change then the previous equation has a unique solution...

An ε-insensitive approach to fuzzy clustering

Jacek Łęski (2001)

International Journal of Applied Mathematics and Computer Science

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Fuzzy clustering can be helpful in finding natural vague boundaries in data. The fuzzy c-means method is one of the most popular clustering methods based on minimization of a criterion function. However, one of the greatest disadvantages of this method is its sensitivity to the presence of noise and outliers in the data. The present paper introduces a new ε-insensitive Fuzzy C-Means (εFCM) clustering algorithm. As a special case, this algorithm includes the well-known Fuzzy C-Medians...

Interactive decision-making in multiobjetive fuzzy programming.

José M. Cadenas, Fernando Jiménez (1994)

Mathware and Soft Computing

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We present an interactive decision support system which aids in solving a general multiobjective fuzzy problem, that is, a multiobjective programming problem with fuzzy goals subject to a fuzzy constraint set. The interactive decision support system is proposed. After eliciting the fuzzy goals of the decision maker for each objective function and the fuzzy elements for each constraint, the satisfactory solutions for the decision maker were derived by interactively updating the reference...