A new estimator of entropy of continuous random variable is suggested in this article. The newly suggested estimator is a modified version of Ebrahimi et al. entropy estimator. This estimator is compared with its coun...
详细信息
A new estimator of entropy of continuous random variable is suggested in this article. The newly suggested estimator is a modified version of Ebrahimi et al. entropy estimator. This estimator is compared with its counterparts suggested by Vasicek, Ebrahimi et al., and Al-Omari in terms of root mean squared errors and bias values. The consistency of the estimator is proved, and some of its properties are provided. The suggested estimator is found to be more efficient than its competitors.
In this article, we present a stochastic simulation-based genetic algorithm for solving chance constraint programming problems, where the randomvariables involved in the parameters follow any continuous distribution....
详细信息
In this article, we present a stochastic simulation-based genetic algorithm for solving chance constraint programming problems, where the randomvariables involved in the parameters follow any continuous distribution. Generally, deriving the deterministic equivalent of a chance constraint is very difficult due to complicated multivariate integration and is only possible if the randomvariables involved in the chance constraint follow some specific distribution such as normal, uniform, exponential and lognormal distribution. In the proposed method, the stochastic model is directly used. The feasibility of the chance constraints are checked using stochastic simulation, and the genetic algorithm is used to obtain the optimal solution. A numerical example is presented to prove the efficiency of the proposed method.
Solution procedure consisting of fuzzy goal programming and stochastic simulation-based genetic algorithm is presented, in this article, to solve multiobjective chance constrained programming problems with continuous ...
详细信息
Solution procedure consisting of fuzzy goal programming and stochastic simulation-based genetic algorithm is presented, in this article, to solve multiobjective chance constrained programming problems with continuous random variables in the objective functions and in chance constraints. The fuzzy goal programming formulation of the problem is developed first using the stochastic simulation-based genetic algorithm. Without deriving the deterministic equivalent, chance constraints are used within the genetic process and their feasibilities are checked by the stochastic simulation technique. The problem is then reduced to an ordinary chance constrained programming problem. Again using the stochastic simulation-based genetic algorithm, the highest membership value of each of the membership goal is achieved and thereby the most satisfactory solution is obtained. The proposed procedure is illustrated by a numerical example.
An optimal unit sizing method for cogeneration systems is proposed using energy demands as continuous random variables. In this method, design variables such as equipment capacities and maximum contract utility demand...
详细信息
An optimal unit sizing method for cogeneration systems is proposed using energy demands as continuous random variables. In this method, design variables such as equipment capacities and maximum contract utility demands are determined together with the systems' operational strategies so as to minimize an expected value of the annual total cost subject to the satisfaction of all the possible energy demands. In evaluating the expected value, decision variables and the objective function are considered as piece-wise linear functions of energy demands by applying a sensitivity analysis in linear programming and an enumeration method in mixed-integer programming. This optimization problem is formulated and solved based on a hierarchical optimization algorithm. A numerical study is carried out on a fuel cell cogeneration system installed in an office building. Through the study, the influence of uncertainties in energy demands on a system's economics and optimal equipment capacities are clarified. (C) 2002 Elsevier Science Ltd. All rights reserved.
We introduce new normalized fractional integral concepts on randomvariables. We also introduce the fractional coupled randomvariables. The Jensen integral inequality is generalized. Also, some fractional bounds esti...
详细信息
We introduce new normalized fractional integral concepts on randomvariables. We also introduce the fractional coupled randomvariables. The Jensen integral inequality is generalized. Also, some fractional bounds estimating the expectation and variance are delivered. At the end, some other results related to the coupled randomvariables are proved. For this work, some results of the papers [On some statistical and probability inequalities. Journal of Inequalities and Special Functions, 2016] and [Some inequalities for the expectation and variance of a randomvariable whose PDFs are absolutely continuous using a pre-Chebychev inequality. Tamkang Journal of Mathematics, 2001] are deduced as some special cases.
An optimal unit sizing method for cogeneration systems is proposed using energy demands as continuous random variables. In this method, design variables such as equipment capacities and maximum contract utility demand...
详细信息
An optimal unit sizing method for cogeneration systems is proposed using energy demands as continuous random variables. In this method, design variables such as equipment capacities and maximum contract utility demands are determined together with the systems' operational strategies so as to minimize an expected value of the annual total cost subject to the satisfaction of all the possible energy demands. In evaluating the expected value, decision variables and the objective function are considered as piece-wise linear functions of energy demands by applying a sensitivity analysis in linear programming and an enumeration method in mixed-integer programming. This optimization problem is formulated and solved based on a hierarchical optimization algorithm. A numerical study is carried out on a fuel cell cogeneration system installed in an office building. Through the study, the influence of uncertainties in energy demands on a system's economics and optimal equipment capacities are clarified. (C) 2002 Elsevier Science Ltd. All rights reserved.
Li and Nadarajah [Signal Processing 127 (2016) 185-190] derived expressions for mean and variance of roundoff error for any continuous random variable. Here, we derive expressions for general moments of the roundoff e...
详细信息
Li and Nadarajah [Signal Processing 127 (2016) 185-190] derived expressions for mean and variance of roundoff error for any continuous random variable. Here, we derive expressions for general moments of the roundoff error, allowing one to study other aspects of roundoff error than just mean and variance. The expressions are specialized for 10 commonly used distributions in signal processing. Numerical studies checking the correctness of the derived expressions are given.
Stochastic discretization is a technique of representing a continuous random variable as a random sum of i.i.d. exponential randomvariables. In this article, we apply this technique to study the limiting behavior of ...
详细信息
Stochastic discretization is a technique of representing a continuous random variable as a random sum of i.i.d. exponential randomvariables. In this article, we apply this technique to study the limiting behavior of a stochastic fluid model. Specifically, we consider an infinite-capacity fluid buffer, where the net input of fluid is regulated by a finite-state irreducible continuous-time Markov chain. Most long-run performance characteristics for such a. fluid system can be expressed as the long-run average reward for a suitably chosen reward structure. In this article, we use stochastic discretization of the fluid content process to efficiently determine the long-run average reward. This method transforms the continuous-state Markov process describing the fluid model into a discrete-state quasi-birth-death process. Hence, standard tools, such as the matrix-geometric approach, become available for the analysis of the fluid buffer. To demonstrate this approach, we analyze the output of a buffer processing fluid from K sources on a first-come first-served basis.
Gadzhiev [4] derived expressions for round off error mean and round off error variance when the rounded variable follows the centered uniform and centered Gaussian distributions. Here, we derive general expressions fo...
详细信息
Gadzhiev [4] derived expressions for round off error mean and round off error variance when the rounded variable follows the centered uniform and centered Gaussian distributions. Here, we derive general expressions for round off error mean and round off error variance when the rounded variable is any continuous random variable on the real line or any continuous random variable over a finite interval. Numerical studies are given. (C) 2016 Elsevier B.V. All rights reserved.
暂无评论