1,721,216 research outputs found
Microbiological characteristics of clinical isolates of Cryptococcus neoformans in Taiwan: serotypes, mating types, molecular types, virulence factors, and antifungal susceptibility
No full textBayesian system reliability assessment under fuzzy environments
No full text[[abstract]]The Bayesian system reliability assessment under fuzzy environments is proposed in this paper. In order to apply the Bayesian approach, the fuzzy parameters are assumed as fuzzy random variables with fuzzy prior distributions. The (conventional) Bayes estimation method will be used to create the fuzzy Bayes point estimator of system reliability by invoking the well-known theorem called 'Resolution Identity' in fuzzy sets theory. On the other hand, we also provide the computational procedures to evaluate the membership degree of any given Bayes point estimate of system reliability. In order to achieve this purpose, we transform the original problem into a nonlinear programming problem. This nonlinear programming problem is then divided into four subproblems for the purpose of simplifying computation. Finally, the subproblems can be solved by using any commercial optimizers, e.g. GAMS or LINGO. (C) 2003 Elsevier Ltd. All rights reserved.[[note]]SC
An (alpha, beta)-optimal solution concept in fuzzy optimization problems
No full text[[abstract]]We propose an (alpha, beta)-optimal solution concept of fuzzy optimization problem based on the possibility and necessity measures. It is well known that the set of all fuzzy numbers can be embedded into a Banach space isometrically and isomorphically. Inspired by this embedding theorem, we can transform the fuzzy optimization problem into a biobjective programming problem by applying the embedding function to the original fuzzy optimization problem. Then the (alpha, beta)-optimal solutions of fuzzy optimization problem can be obtained by solving its corresponding biobjective programming problem. We also consider the fuzzy optimization problem with fuzzy coefficients (i.e., the coefficients are assumed as fuzzy numbers). Under a setting of core value of fuzzy numbers, we provide the Karush-Kuhn-Tucker optimality conditions and show that the optimal solution of its corresponding crisp optimization problem (the usual optimization problem) is also a (1,1)-optimal solution of the original fuzzy optimization problem.[[note]]SC
The central limit theorems for fuzzy random variables
No full text[[abstract]]The new concept of the central limit theorem for fuzzy random variables is discussed in this paper by proposing the convergence in distribution for fuzzy random variables. We first consider the limit properties of fuzzy numbers by invoking the Hausdorff metric and then we extend it to the weak and strong convergence of fuzzy distribution functions. We provide a notion of fuzzy normal distribution. Then the central limit theorem for fuzzy random variables follows naturally. (C) 1999 Elsevier Science Inc. All rights reserved.[[note]]SC
The fuzzy Riemann integral and its numerical integration
No full text[[abstract]]The fuzzy Riemann integral and its numerical integration are proposed in this paper. The alpha-level set of this fuzzy Riemann integral is a closed interval whose end points are the classical Riemann integrals, thus we provide a numerical method to approximate the fuzzy Riemann integral by invoking the Simpson's rule. We fit the end points (closed interval) of the alpha-level set of the fuzzy Riemann integral as polynomials with variable alpha in a least-squares sense. Finally, the membership function of the fuzzy Riemann integral can be transformed into nonlinear programming problem and can be solved by any current optimizer. (C) 2000 Elsevier Science B.V. All rights reserved.[[note]]SC
Fuzzy estimates of regression parameters in linear regression models for imprecise input and output data
No full text[[abstract]]The method for obtaining the fuzzy estimates of regression parameters with the help of "Resolution Identity" in fuzzy sets theory is proposed. The alpha-level least-squares estimates can be obtained from the usual linear regression model by using the alpha-level real-valued data of the corresponding fuzzy input and output data. The membership functions of fuzzy estimates of regression parameters will be constructed according to the form of "Resolution Identity" based on the alpha-level least-squares estimates. In order to obtain the membership degree of any given value taken from the fuzzy estimate, optimization problems have to be solved. Two computational procedures are also provided to solve the optimization problems. (C) 2002 Elsevier Science B.V. All rights reserved.[[note]]SC
Renewal reward processes with fuzzy rewards and their applications to T-age replacement policies
No full text[[abstract]]The application of fuzzy set theory to renewal reward processes is proposed in this paper. The reward is modeled as a fuzzy random variable. A theorem which presents the long-run average fuzzy reward per unit time is stated. A procedure to obtain the best T-age replacement policy with fuzzy cost structure is developed. The original problem is transformed into a nonlinear program in order to evaluate the membership of the long-run average fuzzy cost per unit time. (C) 1999 Elsevier Science B.V. All rights reserved.[[note]]SC
Probability density functions of fuzzy random variables
No full text[[abstract]]The concept of (fuzzy) probability density function of fuzzy random variable is proposed in this paper. Due to the "resolution identity", we can construct a closed fuzzy number from a family of closed intervals. Using the same technique, we can construct the (fuzzy) probability density function of fuzzy random variable from the known probability density function. In order to find the membership of (fuzzy) probability density of fuzzy observation from fuzzy random variable, we transform the original problem into nonlinear programming problem. Then we provide a computational method to evaluate the membership. (C) 1999 Elsevier Science B.V. All rights reserved.[[note]]SC
Duality theorems in fuzzy mathematical programming problems based on the concept of necessity
No full text[[abstract]]The fuzzy-valued Lagrangian function for the fuzzy mathematical programming problem via the concept of the fuzzy scalar (inner) product is proposed. A solution concept of fuzzy optimization problems, which is essentially similar to the notion of Pareto solution in multiobjective programming problems, is also introduced by ranking the fuzzy numbers using the necessity indices. Under these settings, the weak and strong duality theorems can be elicited. We show that the primal and dual fuzzy mathematical programming problems have no duality gap under suitable convexity assumptions for fuzzy-valued functions. (C) 2002 Elsevier B.V. All rights reserved.[[note]]SC
Evaluate fuzzy Riemann integrals using the Monte Carlo method
No full text[[abstract]]Techniques for using the Monte Carlo method to evaluate fuzzy Riemann integrals and improper fuzzy Riemann integrals are proposed in this paper. Owing to the alpha-level set of the (improper) fuzzy Riemann integral being the closed interval whose end points are the classical (improper) Riemann integrals, it is possible to invoke the Monte Carlo method to approximate the end points of the alpha-level closed intervals. We develop the strong law of large numbers for fuzzy random variables in order to give the techniques proposed for evaluating the (improper) fuzzy Riemann integrals using the Monte Carlo approach more theoretical support. The membership function of the (improper) fuzzy Riemann integral can be transformed into mathematical programming problems. Therefore, we can obtain the membership value by solving the mathematical programming problems using the commercial optimizer. (C) 2001 Elsevier Science.[[note]]SC
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