In the present paper the discretization of a particular model arising in the economic field of innovation diffusion is developed. It consists of an optimal control problem governed by an ordinary differential equation...
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ISBN:
(纸本)3540221298
In the present paper the discretization of a particular model arising in the economic field of innovation diffusion is developed. It consists of an optimal control problem governed by an ordinary differential equation. We propose a direct optimization approach characterized by an explicit, fixed step-size continuous Runge-Kutta integration for the state variable approximation. Moreover, high-order Gaussian quadrature rules are used to discretize the objective function. In this way, the optimal control problem is converted into a nonlinear programming one which is solved by means of classical algorithms.
This paper proposes a new parallel search algorithm using evolutionary programming and quasi-simplex technique (EPQS). EPQS produces the offspring from three ways in parallel: 1) Using the Gaussian mutation, 2) Using ...
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ISBN:
(纸本)0646423134
This paper proposes a new parallel search algorithm using evolutionary programming and quasi-simplex technique (EPQS). EPQS produces the offspring from three ways in parallel: 1) Using the Gaussian mutation, 2) Using the Cauchy mutation, and 3) Using the quasi-simplex techniques. The quasi-simplex technique uses the ideal of classical simplex technique and produces four prospective individuals by using the reflection, expansion and compression operations. EPQS selects the parents for the next generation from all the parents and offspring. EPQS takes the diversity of offerings into consideration by generating the offspring from as many as possible ways while it maintains a substantial convergence rate. Experimental studies on six typical benchmark functions have shown that the proposed algorithm is more effective than the competing algorithms.
The unified power flow controller (UPFC) is one of the most promising Flexible AC Transmission Systems (FACTS) devices for the load flow control. Simultaneous optimisation of location and parameters for UPFCs is an im...
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The unified power flow controller (UPFC) is one of the most promising Flexible AC Transmission Systems (FACTS) devices for the load flow control. Simultaneous optimisation of location and parameters for UPFCs is an important issue when the given number of UPFCs is applied to the power system with the purpose of increasing system loadability. This paper presents a mathematical model about optimal location and parameters of UPFCs to maximise the system loadability subject to the transmission line capacity limits and specified voltage level. An improved computational intelligence approach: self-adaptive evolutionary programming (SAEP) is used to solve the nonlinear programming problem presented above for better accuracy. Case studies of the IEEE 30- and 118-bus systems using the proposed model and technique demonstrate that the proposed mathematical model is corrective and efficient. Furthermore, steady-state performance of power system can be effectively enhanced due to the optimal location and parameters of UPFCs.
A sufficient condition for robust stability of nonlinear constrained Model Predictive Control (MPC) with respect to plant/model mismatch is derived. This work is an extension of a previous study on the unconstrained n...
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We propose a quasi-mjiximum-likelihood (quasi-ML) detection scheme with soft output and low decoding complexity for multiple antenna wireless channels modulated with phase-shift keying (PSK). These detectors, which we...
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Crude oil blending is an important unit in petroleum refining industry. Most of blend automation system is a real-time optimizer (RTO). RTO is a model-based optimization approach that uses current process information ...
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Crude oil blending is an important unit in petroleum refining industry. Most of blend automation system is a real-time optimizer (RTO). RTO is a model-based optimization approach that uses current process information to update the model and predict the optimal operating policy. But in many oil fields, they hope to make decision and do supervision control based on the history data, i.e., they want to know the optimal inlet flow rates without on-line analyzers. To overcome the drawkack of the conventional RTO, in this paper we use neural networks to model the blending process by the history data. Then the optimization is carried out via the neural model. The ontributions of this paper are: (1) we propose a new approach to solve the problem of blending optimization based on history data. (2) Sensitivity analysis of the neural optimization is given. (3) Real data of a oil field is used to show effectiveness of the proposed method.
Levin’s MLS projection operator allows defining a surface from a set of points and represents a versatile procedure to generate points on this surface. Practical problems of MLS surfaces are a complicated non-linear ...
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作者：
Stursberg, Olaf
University of Dortmund DortmundD-14221 Germany
Procedures like start-up, product change-over, or shutdown of processing systems usually involve the manipulation of continuous and discrete controls. To optimize such procedures, the use of hybrid models is often app...
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This paper proposes the software package SISCON, dedicated to the evaluation of optimal decisions for large scale systems. SISCON firstly evaluates mathematical models developed from experimental data using LS methods...
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In this paper, we address convolutive blind source separation (BSS) of speech signals in the frequency domain and explicitly exploit the second order statistics (SOS) of nonstationary signals. Based on certain constra...
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