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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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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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Convex Optimization algorithms for Sparse Signal Reconstruction 9

Convex Optimization Algorithms for Sparse Signal Reconstruct...

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9th Mediterranean Conference on Embedded Computing (MECO)

作者： Jovanovic, Filip Miladinovic, Dragana Radunovic, Natasa Univ Montenegro Fac Elect Engn Dzordza Vasingtona Bb Podgorica 20000 Montenegro

ISBN: (纸本)9781728169491

Compressive sensing is a very important field of research in signal processing as it is based on the idea that a signal, sparse in a certain transform domain, can be completely recovered based on a small set of available measurements. Many algorithms, dealing with sparse signal recovery have been proposed through the years. This paper focuses on convex optimization algorithms and explores their performance in two different transform domains - discrete Fourier and discrete cosine transforms. The observed algorithms are the adaptive gradient based algorithm, primal-dual interior point method and the log barrier algorithm, all which are used to solve different formulations of the l1-minimization problem.

关键词： Compressive Sensing convex optimization reconstruction gradient based algorithm basis pursuit DFT DCT

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A modified gradient based algorithm for solving Sylvester equations

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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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Stochastic Flow Models with Delays and Applications to Multi-Intersection Traffic Light Control

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IFAC-PapersOnLine 2018年第7期51卷 39-44页

作者： Chen, Rui Cassandras, Christos G. Division of Systems Engineering Boston University BrooklineMA United States

We extend Stochastic Flow Models (SFMs), used for a large class of discrete event and hybrid systems, by including the delays which typically arise in flow movement. We apply this framework to the multi-intersection traffic light control problem by including transit delays for vehicles moving from one intersection to the next. Using Infinitesimal Perturbation Analysis (IPA) for this SFM with delays, we derive new on-line gradient estimates of several congestion cost metrics with respect to the controllable green and red cycle lengths. The IPA estimators are used to iteratively adjust light cycle lengths to improve performance and, in conjunction with a standard gradient-based algorithm, to obtain optimal values which adapt to changing traffic conditions. We introduce two new cost metrics to better capture congestion and show that the inclusion of delays in our analysis leads to improved performance relative to models that ignore delays. © 2018

关键词： Stochastic models Applications Cost benefit analysis Discrete event simulation Hybrid systems Iterative methods Optimization Stochastic systems Street traffic control Traffic congestion gradient based algorithm gradient estimates Improve performance Infinitesimal perturbation analysis Performance evaluations Stochastic flow model Traffic conditions Traffic light control

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AN OPTIMAL APPROACH FOR EEG/ERP NOISE CANCELLATION USING ADAPTIVE FILTER WITH OPPOSITIONAL WHALE OPTIMIZATION algorithm

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BIOMEDICAL ENGINEERING-APPLICATIONS BASIS COMMUNICATIONS 2019年第5期31卷

作者： Nagal, Rachana Kumar, Pradeep Bansal, Poonam AMITY Univ AMITY Sch Engn & Technol Noida 201303 Uttar Pradesh India Maharaja Surajmal Inst Technol Dept Comp Sci & Engn New Delhi 110086 India

In this paper, the Oppositional Whale Optimization algorithm (OWOA) is applied to Adaptive Noise Canceller (ANC) for the filtering of Electroencephalography/Event-Related Potentials (EEG/ERP) signals. Performance of ANC will be improved by calculating the optimal weight value and proposed OWOA technique is used to update weight value. Adaptive filter's noise reduction capability has been tested through consideration of White Gaussian Noise (WGN) over contaminated EEG signals at various SNR levels (-10 dB, -15 dB and -20 dB). The performance of the proposed OWOA algorithm is assessed in terms of Signal to Noise Ratio (SNR) in dB, mean value, and the correlation between resultant and input ERP. In this work, ANCs are also implemented by utilizing conventional gradient-based techniques like Recursive Least Square (RLS), Least Mean Square (LMS) and other optimization algorithms such as Genetic algorithm (GA), Particle Swarm Optimization (PSO) and WOA techniques. In average cases of noisy environment, comparative analysis shows that the proposed OWOA technique provides higher SNR value and significantly lower mean, and correlation as compared to gradient-based and swarm-based techniques. The comparative results show that extracting the desired EEG component is more effective in the proposed OWOA method. So, it has seen that OWOA-based noise reduction technique removing the artifacts and improving the quality of EEG signals significantly for biomedical analysis.

关键词： Optimization algorithm Adaptive noise canceller White Gaussian noise gradient based algorithm Nature inspired algorithm

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The Hermite and Fourier Transforms in Sparse Reconstruction of Sinusoidal Signals 7

The Hermite and Fourier Transforms in Sparse Reconstruction ...

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7th Mediterranean Conference on Embedded Computing (MECO)

作者： Vesovic, Maja Konatar, Valentina Univ Montenegro Fac Elect Engn Podgorica 20000 Montenegro

ISBN: (纸本)9781538656839

The paper observes the Hermite and the Fourier Transform domains in terms of Frequency Hopping Spread Spectrum signals sparsification. Sparse signals can be recovered from a reduced set of samples by using the Compressive Sensing approach. The under-sampling and the reconstruction of those signals are also analyzed in this paper. The number of measurements (available signal samples) is varied and reconstruction performance is tested in all considered cases and for both observed domains. The signal recovery is done using an adaptive gradient based algorithm. The theory is verified with the experimental results.

关键词： Hermite transform Compressive Sensing FHSS signal reconstruction gradient based algorithm

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Traffic Flow Model Validation Using METANET, ADOL-C and RPROP

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IFAC-PapersOnLine 2016年第3期49卷 291-296页

作者： Poole, Adam Kotsialos, Apostolos School of Engineering and Computing Sciences Durham University Stockton Road DurhamDH1 3LE United Kingdom

Macroscopic traffic flow model calibration is an optimisation problem typically solved by a derivative-free population based stochastic search methods. This paper reports on the use of a gradient based algorithm using automatic differentiation. The ADOL-C library is coupled with the METANET source code and this system is embedded within an optimisation algorithm based on RPROP. The result is a very efficient system which is able to be calibrate METANET's second order model by determining the density and speed equation parameters as well as the fundamental diagrams used. Information obtained from the system's Jacobian provides extra insight into the system dynamics. A 22 km site is considered near Sheffield, UK and the results of a typical calibration and validation process are reported. © 2016

关键词： C (programming language) Calibration gradient methods Optimization Parameter estimation Stochastic models Stochastic systems Traffic control Automatic differentiations Calibration and validations gradient based algorithm Macroscopic traffic flow model Optimisation problems Road traffic Stochastic search methods Traffic flow modeling

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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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