In this paper, an upper level decision problem is formed by a set of possibilistic constraint conditions with possibilistic ideal goals of decision variables given by a decision maker. Possibilistic programming decisi...
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In this paper, an upper level decision problem is formed by a set of possibilistic constraint conditions with possibilistic ideal goals of decision variables given by a decision maker. Possibilistic programming decision problems are proposed to obtain the possibilistic decision which approaches the possibilistic ideal goals as much as possible subject to possibilistic constraints. Two possibility distributions are considered for re-fleeting the inherent uncertainty in the decision problem. The possibilistic programmingproblems can be converted into conventional quadratic programming problems. The analysis results show that the proposed methods are effective for upper level decision problems under uncertainty which are extensively encountered in business and economics.
Power load forecasting is an important guarantee of safe, stable, and economic operation of power systems. It is appropriate to use interval data to represent fuzzy information in power load forecasting. The dual poss...
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Power load forecasting is an important guarantee of safe, stable, and economic operation of power systems. It is appropriate to use interval data to represent fuzzy information in power load forecasting. The dual possibilistic regression models approximate the observed interval data from the outside and inside directions, respectively, which can estimate the inherent uncertainty existing in the given fuzzy phenomenon well. In this article, efficient dual possibilistic regression models of support vector machines based on solving a group of quadratic programming problems are proposed. And each quadratic programming problem containing fewer optimization variables makes the training speed of the proposed approach fast. Compared with other interval regression approaches based on support vector machines, such as quadratic loss support vector machine approach and two smaller quadratic programming problem support vector machine approach, the proposed approach is more efficient on several artificial datasets and power load dataset.
An interval reduced basis approach (IRBA) is presented for analyzing acoustic response of coupled structural-acoustic system with interval parameters. Simultaneously an integrated framework based on IRBA is establishe...
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An interval reduced basis approach (IRBA) is presented for analyzing acoustic response of coupled structural-acoustic system with interval parameters. Simultaneously an integrated framework based on IRBA is established to deal with uncertain acoustic propagation using deterministic finite element (FE) software. The present IRBA aims to improve the accuracy of the conventional first-order approximation and also allow the efficient calculation of second-order approximation of acoustic response. In IRBA, acoustic response is approximated using a linear combination of interval basis vectors with undetermined coefficients. To get explicit expression of acoustic response in terms of interval parameters, the three terms of the second-order perturbation method are employed as basis vectors, and the variant of the Galerkin scheme is applied for derivation of the reduced-order system of equations. For the second-order approximation, the determination of acoustic response interval is reformulated into a series of quadratic programming problems, which are solved using the difference of convex functions (DC) algorithm effectively. The performance of IRBA and availability of the present framework are validated by numerical examples.
In order to optimize the control performance of the permanent magnet synchronous motor（PMSM） in the presence of strong disturbances and model uncertainty,a composite single-loop model predictive control（MPC） based...
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In order to optimize the control performance of the permanent magnet synchronous motor（PMSM） in the presence of strong disturbances and model uncertainty,a composite single-loop model predictive control（MPC） based on disturbance observer（DOB） is studied in this ***,a DOB is employed to estimate the lumped disturbance including the uncertain parameters and the external disturbance in real ***,benefitting from model-based design,estimations are introduced into the prediction model of the PMSM,then a composite single-loop MPC is designed based on this ***,the optimal control input is obtained,by introducing Lagrange vector and solving the quadratic programming problem with *** results show that the proposed composite control method has a faster transient response and a better disturbance rejection *** addition,compared with the generally cascade structure,only the predictive horizon and the observer poles are needed to be adjusted,so it provides a more practical optimization algorithm for the engineering application.
Large number of example vectors brings difficulties for quadratic programming problem with support vector machines,traditional methods may be *** intelligent search technologies,such as genetic algorithms and particle...
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Large number of example vectors brings difficulties for quadratic programming problem with support vector machines,traditional methods may be *** intelligent search technologies,such as genetic algorithms and particle swarm optimization algorithm,can give a similar solve of problems in less *** Swarm Optimization is better than genetic algorithms in convergence and stability of the *** to the characters of swarm intelligence and constrained optimization,we propose a method to solve a linearly constrained quadratic optimization problem in training support vector machines with PSO(for short).Testify PSO has determinate applied value in the field of support vector machines,and it is a new way for quadratic programming problem with a large number of example vectors.
This paper deals a bimatrix game with payoffs as closed intervals. Existence of equilibrium point of this game is discussed by using suitable interval quadratic programming problem. Further, a methodology is proposed ...
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This paper deals a bimatrix game with payoffs as closed intervals. Existence of equilibrium point of this game is discussed by using suitable interval quadratic programming problem. Further, a methodology is proposed for finding optimal strategies for each player of the game. The methodology is illustrated by numerical example.
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