This paper presents a formulation for an Optimal Power Flow problem that includes the DFIG wind generator reactive power characteristics in order to obtain set points that consider wind turbines loading capabilities. ...
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ISBN:
(纸本)9781424421893
This paper presents a formulation for an Optimal Power Flow problem that includes the DFIG wind generator reactive power characteristics in order to obtain set points that consider wind turbines loading capabilities. This approach can be adopted at the wind park control level to define the active and reactive set points following requests from the wind park dispatch centers.
Wireless ad hoc networks have attracted a lot of attention recently. Resource allocation in such networks needs to address both fairness and overall network performance. Pricing is a prospective direction to regulate ...
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ISBN:
(纸本)9781424412501
Wireless ad hoc networks have attracted a lot of attention recently. Resource allocation in such networks needs to address both fairness and overall network performance. Pricing is a prospective direction to regulate behaviors of individual nodes while providing incentives for cooperation. In this work, we develop some pricing strategies for resource allocation by taking account of factors like multiple transmission rates and energy consumption of nodes, which have not been well studied in former works. We propose a clique-based model which allows us to achieve optimal resource utilization and fairness among network flows. We also sketch how our model can be extended to incorporate energy consumptions of flows. Simulation results are presented to show the effectiveness of these strategies.
In this paper we give a brief overview of speaker recognition with special emphasis on nonlinear predictive models, based on neural nets. Main challenges and possibilities for nonlinear feature extraction are describe...
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ISBN:
(纸本)9783540715030
In this paper we give a brief overview of speaker recognition with special emphasis on nonlinear predictive models, based on neural nets. Main challenges and possibilities for nonlinear feature extraction are described, and experimental results of several strategies are provided. This paper is presented as a starting point for the non-linear model for speaker recognition.
In this paper, we consider a nonlinear semi-definite programming problem that represents the fixed order H-2, and H-2/H-infinity. synthesis problems. A proximal-point sequential quadratic programming method that makes...
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In this paper, we consider a nonlinear semi-definite programming problem that represents the fixed order H-2, and H-2/H-infinity. synthesis problems. A proximal-point sequential quadratic programming method that makes use of trust region is developed. Furthermore, the constrained trust region method [F. Leibfritz, E.M.E. Mostafa, Trust region methods for solving the optimal output feedback design problem, Int. J. Contr. 76 (2003) 501-519], which was designed to solve a nonlinear semi-definite program representing the H-2 synthesis problem, is extended to solve a more general nonlinear semi-definite program representing the fixed order H-2/H-infinity synthesis problem. Numerical results for the proposed methods are given. (c) 2005 Elsevier Inc. All rights reserved.
The main goal of this study is to investigate the time-optimal control problem of an omni-directional mobile robot between two configurations. In the proposed method, this problem is formulated and solved as a constra...
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ISBN:
(纸本)9781424411832
The main goal of this study is to investigate the time-optimal control problem of an omni-directional mobile robot between two configurations. In the proposed method, this problem is formulated and solved as a constrained nonlinear programming (NLP) one. During the optimization process, the count of control steps is fixed initially and the sampling period is treated as a variable to be determined. The goal is to minimize the sampling period such that it is below a specific minimum value, which is set in advance considering the accuracy of discretization. To generate initial feasible solutions of the NLP problem, a systematic approach is also proposed. Since different initial feasible solutions can be generated, the optimization process of the NLP problem can be started from many different points to find the optimal solution. To show the feasibility of the proposed method, simulation and experimental results are included for illustration.
In this paper we present an extension of the Nelder and Mead simplex algorithm for non-linear programming, which makes it suitable for both unconstrained and constrained optimisation.(1) We then explore several extens...
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ISBN:
(数字)9783540744467
ISBN:
(纸本)9783540744450
In this paper we present an extension of the Nelder and Mead simplex algorithm for non-linear programming, which makes it suitable for both unconstrained and constrained optimisation.(1) We then explore several extensions of the method for escaping local optima, which make it a simple, yet powerful tool for optimisation of nonlinear functions with many local optima. A strategy which proved to be extremely robust was random start local search, with a correct, though unusual, setup. Actually, for some of the benchmarks, this simple metaheuristic remained the most effective one. The idea is to use a very large simplex at the begin;the initial movements of this simplex are very large, and therefore act as a kind of filter, which naturally drives the search into good areas. We propose two more mechanisms for escaping local optima, which, still being very simple to implement, provide better results for some difficult problems.
In recent years, memetic algorithms (MAs) have been proposed to enhance the performance of evolutionary algorithms by incorporating local search techniques with evolutionary algorithms' global search ability, and ...
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ISBN:
(纸本)9781424413393
In recent years, memetic algorithms (MAs) have been proposed to enhance the performance of evolutionary algorithms by incorporating local search techniques with evolutionary algorithms' global search ability, and applied successfully to solve different type of optimization problems. This paper proposes a new memetic algorithm and then introduces an agent-based memetic algorithm (AMA), for the first time, to further enhance the ability of MA in solving constrained optimization problems. In a lattice-like environment, each of the agents represents a candidate solution of the problem. The agents are able to sense and act on the society, and their performances i.e. fitness of the solution improves through co-evolutionary adaptation of society with the individual learning of the agents. The proposed algorithm is tested on 13 benchmark problems and the experimental results show promising performance.
This paper describes the optimal tuning for the output limits of the power system stabilizer (PSS), which can improve the system damping performance immediately following a large disturbance. The non-smooth nonlinear ...
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ISBN:
(纸本)9781424413799
This paper describes the optimal tuning for the output limits of the power system stabilizer (PSS), which can improve the system damping performance immediately following a large disturbance. The non-smooth nonlinear parameters such as the saturation limits of the PSS cannot be tuned by the conventional methods based on linear approaches. To implement the systematic optimal tuning for the output limits of the PSS, a feedforward neural network (FFNN) is applied to the hybrid system model based on the differential-algebraic- impulsive-switched (DAIS) structure. The FFNN is firstly designed to identify the trajectory sensitivities obtained from the DAIS structure. Thereafter, it estimates the second-order derivatives of an objective function J, which is used during iterations of optimization process. The performance of the optimal output limits tuned by the proposed method is evaluated by applying a large disturbance to a power system.
The localization of dipolar sources in the brain based on EEG or MEG data is a frequent problem in the neurosciences. Especially deterministic approaches often have problems in finding the global optimum of the associ...
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ISBN:
(纸本)9781424409488
The localization of dipolar sources in the brain based on EEG or MEG data is a frequent problem in the neurosciences. Especially deterministic approaches often have problems in finding the global optimum of the associated non-linear optimization function, when two or more dipoles are to be reconstructed. In such cases, probabilistic approaches turned out to be superior, but their applicability in neuromagnetic source localizations is not yet satisfactory. The objective of this study was the design of multi-level evolution strategies that perform better in such applications. We newly created nested fast evolution strategies which realize a combination of locally searching inner evolution strategies and globally searching outer fast evolution strategies. They were benchmarked and compared to single-level fast evolution strategy by conducting a two dipole fit with a MEG data set from a neuropsychological experiment. In the comparison, fast nested evolution strategies showed superior performance.
The classical mathematical transportation problem is one that attempts to deliver a fixed number of units or a product from the Supplier's various warehouses (or storage facilities) to a number of customers at min...
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ISBN:
(纸本)9789077381366
The classical mathematical transportation problem is one that attempts to deliver a fixed number of units or a product from the Supplier's various warehouses (or storage facilities) to a number of customers at minimal cost or maximum profit, meeting all of the customers individual requirements. The classic transportation problem assumes different constant unit costs for each shipping route (from each different warehouse to each different customer). Then the problem is usually solved with some linear programming simplex Computer package or one of the specialized versions of it designed specifically for the classic transportation problem. However in the practical global business world, the unit shipping costs from each warehouse to each Customer may vary greatly with the amount shipped. Therefore presented here will be a ten warehouse to ten different customer locations shipping problem where the unit shipping costs vary with the amount of product shipped. Therefore linear programming is replaced with the computer simulation based solution technique multi stage Monte Carlo optimization (MSMCO) to solve this specific problem presented here.
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