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A modified gradient based algorithm for solving Sylvester equations

APPLIED MATHEMATICS AND COMPUTATION 2012年第9期218卷 5620-5628页

作者： Wang, Xiang Dai, Lin Liao, Dan Nanchang Univ Dept Math Nanchang 330031 Peoples R China

In this paper a modified gradient based algorithm for solving Sylvester equations is presented. Different from the gradient based method introduced by Ding and Chen [7] and the relaxed gradient based algorithm proposed by Niu et al. [18], the information generated in the first half-iterative step is fully exploited and used to construct the approximate solution. Theoretical analysis shows that the new method converges under certain assumptions. Numerical results are given to verify the efficiency of the new method. (C) 2011 Elsevier Inc. All rights reserved.

关键词： Sylvester equations gradient based algorithm Modified gradient based algorithm

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A new genetic type method with integrated gradient based algorithm method for storage optimization of supply chain 7

A new genetic type method with integrated gradient based alg...

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7th International Conference on Computational Intelligence and Communication Networks (CICN)

作者： Xu, Ya-ping Liu, Xin North China Univ Water Resources & Elect Power Zhengzhou Peoples R China China Univ Petr Beijing Peoples R China

ISBN: (纸本)9781509000760

based on the current situation and existing problems of supply chain's storage optimization management, a new genetic type method with integrated gradient based algorithm method for storage optimization of supply chain is proposed. This method combines the fast convergence and strong ability of local search of gradient method and global search ability of genetic algorithm. The problem of storage optimization of supply chain is applied. The research results show that the proposed method is effective and suitable.

关键词： supply chain storage optimization genetic type method gradient based algorithm

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Energy Transfer in Two-Level Quantum Systems via Speed gradient-based algorithm

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IFAC-PapersOnLine 2015年第11 11期48卷 446-450页

作者： Pechen, Alexander Borisenok, Sergey Steklov Mathematical Institute of Russian Academy of Sciences Gubkina str. 8 Moscow119991 Russia Department of Electrical and Electronics Engineering Faculty of Engineering Abdullah Gül University Kayseri38080 Turkey

We develop the speed gradient-based algorithm for controlled transfer of energy in a two-level quantum system towards a predefined value of energy using as control spectral density of incoherent photons. The algorithm can stabilize energy at a value less than one half of the energy gap between the two system states and is shown to be more effective for cooling than for heating. © 2015

关键词： Quantum optics Energy transfer Spectral density gradient based algorithm Quantum system System state

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gradient based variable forgetting factor nonlinear RLS algorithm using correlation function with nonzero lags

Gradient based variable forgetting factor nonlinear RLS algo...

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Leung, SH So, CF City Univ Hong Kong Dept Elect Engn Hong Kong Hong Kong Peoples R China

ISBN: (纸本)0780374029

In environment with impulsive noise, most learning algorithms are encountered difficulty in distinguishing the nature of large error signal, whether caused by the impulse noise or large model error. Consequently, they suffer from slow convergence or large misadjustment. A new gradient based variable forgetting factor nonlinear RLS algorithm uses correlation function of error signal with nonzero lags (GCVFF) is introduced. The correlation of nonzero lags maintains the sensitivity of the algorithm responding to the model error and becomes sluggish to the impulse noise. Simulation results show that it achieves fast convergence speed and small misadjustment and outperforms other variable forgetting factor (VFF) RLS algorithms.

关键词： gradient methods nonlinear estimation nonlinear filters recursive filters least squares approximations correlation methods convergence of numerical methods adaptive filters impulse noise recursive estimation gradient based algorithm nonlinear RLS algorithm variable forgetting factor correlation function model error error signal nonzero lags convergence speed recursive least squares filtering adaptive filtering impulsive noise learning algorithms

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Well placement optimization according to field production curve using gradient-based control methods through dynamic modeling

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2012年 100卷 178-188页

作者： Ebadat, A. Karimaghaee, P. Shiraz Univ Sch Elect & Control Engn Shiraz Iran

Determination of optimal well locations plays an important role in field development. Therefore in recent years, there has been an increasing consideration to solve the problem through systematic methods. The problem usually defined as finding well locations such that maximum profit is achieved. Usually there is an industrial demand for oil and gas production in oil and gas companies with respect to consumers' needs. In such cases tracking the required production curve could be a good choice. For this purpose, in this paper optimal well placement probleni is formulated as an optimal control one. In this case, well locations and the field oil production total/rate are input and output variables, respectively. The target is to locate wells such that the output can track a desired pre-specified curve. The curve can be determined according to the industrial gas and oil companies' seasonal need. To our best knowledge this is the first contribution that define well placement as a tracking problem in an optimal control frame work. The first step in solving such problems is modeling. No one can ever deny the significance of an appropriate model. According to reservoir complexity usually neural network and neuro-fuzzy modeling were used. However, because of reservoir uncertainties, dynamical approaches where the model would be updated during the process, could be more helpful. This study presents an approach where a Dynamic Fuzzy Neural Network (DFNN) is applied to generate a model which can perform dynamically. The model will be updated at each iteration due to current reservoir information. On the other hand, since the network starts with no hidden units, there is no need to generate input-output set of data for training purposes for which hundreds of costly and time-consuming simulations are required so simulator runs will be decreased significantly. In the optimization step, gradient based approaches as one of the most common methods in control field are employed. These

关键词： well placement Dynamic Fuzzy Neural Network gradient based algorithm

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Attraction Domain in gradient Optimization-based Sample Maximum Likelihood Estimation

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IFAC-PapersOnLine 2015年第28期48卷 314-319页

作者： Zou, Yiqun Tang, Xiafei School of Information Science and Engineering Central South University Changsha410083 China

Sample maximum likelihood(SML) method is frequently used to identify errors-invariables(EIV) system. It generates the estimate through minimizing relevant cost function built on the mean input-output data and sample noise variances. To help gradient-based algorithm overcome local convergence, we examine the attraction domain for the SML cost. It is shown in this paper that the asymptotic convergence properties of the objective can be learned equivalently by the noiseless version. Moreover we present some special attraction domains that contain the global minimum under certain structures. For the particular models, careful initialization locating in the same domain leads the algorithm to find the global minimum. © 2015

关键词： Maximum likelihood estimation Cost benefit analysis Cost functions Global optimization Asymptotic convergence Attraction domain EIV system gradient based algorithm gradient optimization gradient based optimization Input output data Local Convergence

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Identification of Wiener time delay systems based on hierarchical gradient approach

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IFAC-PapersOnLine 2015年第1期28卷 403-408页

作者： Atitallah, Asma Bedoui, Saïda Abderrahim, Kamel National Engineering Schoo Gabes University of Gabes Gabes6029 Tunisia

In this paper, we propose a hierarchical gradient based algorithm to identify simultaneously the parameters of the linear and nonlinear blocks and the time delay of Wiener time delay systems. We separate the nonlinear cost function into two cost functions and present the gradient algorithm for estimating directly the time delay and the parameters. Furthermore, we give an appropriate choice of convergence factor. Numerical simulation results are presented to demonstrate the performance of the proposed method. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Parameter estimation Cost functions Delay control systems Hierarchical systems Identification (control systems) Nonlinear systems Numerical methods Time delay Timing circuits Convergence factor gradient algorithm gradient approach gradient based algorithm Hierarchical approach Nonlinear cost functions Time delay systems Wiener systems

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Application of gradient-based Control Methods in Efficient Oil Well Placement through Dynamic Fuzzy Neural Network Modeling

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International Conference on Digital Enterprise and Information Systems

作者： Ebadat, A. Karimaghaee, P. Mandiyar, H. Shiraz Univ Sch Elect & Control Engn Shiraz Iran Shiraz Univ Sch Chem Petroleum & Gas Engn Shiraz Iran

ISBN: (纸本)9783642226021

This study presents an approach for determining optimal locations of oil wells in an oil field such that a predetermined production policy can be achieved. In the first step, Dynamic Fuzzy Neural Network (DFNN) is employed to generate an analytical and dynamic model of the reservoir. The model would be updated during the process due to current reservoir information. In order to determine optimal weights for DFNN, Orthogonal Least Square method is modified and applied. It will be shown that the method has more computational efficiency in comparison with other approaches. In the second stage, gradient-based approaches as one of the most common methods in control field are employed to determine well locations based on the generated model. The method can perform search without being obliged to use the simulator several times. Finally, simulation results show the abilities of the proposed procedure in both modeling and tracking control.

关键词： Well placement Dynamic Fuzzy Neural Network gradient based algorithm

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A FAST AND ACCURATE ADAPTIVE NOTCH FILTER USING A MONOTONICALLY INCREASING gradient 22

A FAST AND ACCURATE ADAPTIVE NOTCH FILTER USING A MONOTONICA...

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22nd European Signal Processing Conference (EUSIPCO)

作者： Sugiura, Yosuke Tokyo Univ Sci Fac Sci & Technol Tokyo 162 Japan

ISBN: (纸本)9780992862619

In this paper, we propose a new adaptive notch filter algorithm to achieve the fast and accurate narrow-band noise reduction. In the proposed algorithm, we introduce the monotonically increasing function into the gradient, which provides the fast convergence far away from the optical frequency. We additionally introduce the enhancement function into the gradient to design the steepness of the gradient curve. The proposed gradient can adjust the trade off between the convergence speed and the estimation accuracy more flexibly. Several computational simulations show that the proposed algorithm can simultaneously provide fast convergence and high accurate estimation compared with the conventional NLMS algorithm.

关键词： Adaptive Notch Filter gradient based algorithm Monotonically Increasing gradient Fast and Accurate Estimation

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Thermal parameters identification in the correlation of spacecraft thermal models against thermal test results

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ACTA ASTRONAUTICA 2022年 191卷 270-278页

作者： Garmendia, Inaki Anglada, Eva Univ Basque Country Engn Sch Gipuzkoa Mech Engn Dept UPV EHU Plaza Europa 1 E-20018 San Sebastian Spain Basque Res & Technol Alliance BRTA TECNALIA Mikeletegi Pasealekua 2 E-20009 San Sebastian Spain

Temperatures predicted by the Thermal Mathematical Models (TMMs) used in the thermal control design of spacecraft, usually present differences with the values measured during the thermal test campaign. Therefore, the TMMs must be correlated with the thermal tests to reduce these differences to admissible values. This task can be addressed in an automatized way considering the correlation as an optimization problem, where the differences between predicted and measured temperatures are minimized. This is achieved modifying the values assigned to some parameters used in the TMMs. The main drawback of this approximation is the risk of loosing the physical sense of some model parameters. The reason is that the thermal inverse problem, that is, calculate the thermal parameters that produce a specific temperature distribution, often has not a unique solution. A methodology of automatized correlation to calculate the correct values of the model parameters, in the sense that they maintain its physical interpretation, is presented in this article. The key point relies in setting up an overdetermined system of equations. The expression to calculate the minimum number of load cases required, is developed, and several case studies are presented to validate the proposed methodology. A gradient based public available set of subroutines (TOLMIN) has been used as optimization algorithm.

关键词： Model correlation Thermal mathematical model Space vehicle Thermal control gradient based algorithm

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