检索结果-内蒙古大学图书馆

Decoupling PI controller design for a normal conducting RF cavity using a recursive levenberg-marquardt algorithm

IEEE TRANSACTIONS ON NUCLEAR SCIENCE 2005年第1期52卷 440-449页

作者： Kwon, SI Lynch, M Prokop, M Los Alamos Natl Lab LANSCE Div Los Alamos NM 87545 USA

This paper addresses the system identification and the decoupling PI controller design for a normal conducting RF cavity. Based on the open-loop measurement data of an SNS DTL cavity, the open-loop system's bandwidths and loop time delays are estimated by using hatched least square. With the identified system, a PI controller is designed in such a way that it suppresses the time varying klystron droop and decouples the In-phase and Quadrature of the cavity field. The levenberg-marquardt algorithm is applied for nonlinear least squares to obtain the optimal PI controller parameters. The tuned PI controller gains are downloaded to the low-level RF system by using channel access. The experiment of the closed-loop system is performed and the performance is investigated. The proposed tuning method is running automatically in real time interface between a host computer with controller hardware through ActiveX Channel Access.

关键词： capacitor bank droop high voltage power supply ripple least square levenberg-marquardt algorithm normal conducting RF cavity operating point PI feedback control spallation neutron source system identification

来源：评论

学校读者我要写书评

暂无评论

Krylov-levenberg-marquardt algorithm for Structured Tucker Tensor Decompositions

引用

IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2021年第3期15卷 550-559页

作者： Tichavsky, Petr Phan, Anh-Huy Cichocki, Andrzej Czech Acad Sci Inst Informat Theory & Automat Prague 18200 Czech Republic Skolkovo Inst Sci & Technol Skoltech Moscow 143026 Russia

Structured Tucker tensor decomposition models complete or incomplete multiway data sets (tensors), where the core tensor and the factor matrices can obey different constraints. The model includes block-term decomposition or canonical polyadic decomposition as special cases. We propose a very flexible optimization method for the structured Tucker decomposition problem, based on the second-order levenberg-marquardt optimization, using an approximation of the Hessian matrix by the Krylov subspace method. An algorithm with limited sensitivity of the decomposition is included. The proposed algorithm is shown to perform well in comparison to existing tensor decomposition methods.

关键词： Tensors Matrix decomposition Signal processing algorithms Sensitivity Convergence Approximation algorithms Tuning Alternating direction of multipliers CANDECOMP CANDELINC Krylov subspace levenberg-marquardt algorithm PARAFAC PARALIND sensitivity tensor chain Tucker decomposition

来源：评论

学校读者我要写书评

暂无评论

Forecasting discharge rate and chloride content of karstic spring water by applying the levenberg-marquardt algorithm

引用

ENVIRONMENTAL EARTH SCIENCES 2021年第11期80卷 1-12页

作者： Bekas, Georgios K. Alexakis, Dimitrios E. Gamvroula, Dimitra E. Univ West Att Sch Engn Dept Civil Engn Lab Geoenvironm Sci & Environm Qual Assuranc 250 Thivon & P Ralli Str Athens 12244 Greece

The Mediterranean countries' coastal-karstic aquifer systems are facing increased pressure due to seawater intrusion and drought impacts. There is a need to understand better karstic systems' functional mechanisms for developing the most appropriate management scenario of water resources in these systems. In this study, the application of Non-Linear Autoregressive neural networks (NAR) on a dataset from mid-1968 to 1994 was deployed for predicting values of discharge flow rates and salinity of Almyros spring (Heraklion-Crete, Greece). Two neural networks were trained for the prediction of the discharge rates and the chloride concentration. The neural networks operated with the levenberg-marquardt algorithm's aid and attained a coefficient of determination R=0.83, and R=0.86, respectively, indicating a high degree of prediction capacity.

关键词： NAR neural network Water discharge prediction levenberg-marquardt algorithm Karst aquifer Almyros spring Crete

来源：评论

学校读者我要写书评

暂无评论

Real-time state-of-health monitoring of lithium-ion battery with anomaly detection, levenberg-marquardt algorithm, and multiphase exponential regression model

引用

NEURAL COMPUTING & APPLICATIONS 2021年第4期33卷 1193-1206页

作者： Ossai, Chinedu, I Egwutuoha, Ifeanyi P. Swinburne Univ Fac Hlth Arts & Design Sch Hlth Sci Dept Hlth & Med Sci John St Hawthorn Vic 3122 Australia Victoria Univ Sydney Coll Engn & Sci 160 Sussex St Sydney NSW Australia

The state of health (SOH) of lithium-ion (Li+) battery prediction plays significant roles in battery management and the determination of the durability of the battery in service. This study used segmentation-type anomaly detection, the levenberg-marquardt (LM) algorithm, and multiphase exponential regression (MER) model to determine SOH of the Li+ batteries. By determining the changepoint boundaries using the characteristic values such as voltage transition rate (VTR), temperature transition rate (TTR), and charge capacities of the Li+ battery at the changepoint timestamps, we determined the parametric values of the biphasic MER. The characteristic transition rate values, which depend on the transition probabilities of the rolling standard deviations of the measured voltage and temperature, were later utilized with the matching charge capacities to model various training-testing dataset combinations. This helped to estimate the SOH of the battery at different life-cycle phases. This study also developed a technique for real-time estimation of the remaining useful life of the battery by using the MER model parameters, VTR, and TTR which were previously unseen parametric values of the Li+ battery. The result obtained from the proposed model indicates that our technique will be effective for online SOH estimation of Li+ batteries.

关键词： Anomaly detection Lithium-ion battery levenberg-marquardt algorithm Multiphase exponential regression State of health Voltage Temperature

来源：评论

学校读者我要写书评

暂无评论

Comparative prediction of surface roughness for MAFM finished aluminium/silicon carbide/aluminium trioxide/rare earth oxides (Al/SiC/Al2O3)/REOs) composites using a levenberg-marquardt algorithm and a Box-Behnken Design

引用

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART E-JOURNAL OF PROCESS MECHANICAL ENGINEERING 2022年第3期236卷 790-804页

作者： Sharma, Mayur Janardhan, Gorti Sharma, Vipin K. Kumar, Vinod Joshi, Ravinder S. Daikin Air Conditioning India Private Ltd Gurgaon Haryana India Natl Inst Technol Rourkela Rourkela India Thapar Inst Engn & Technol Dept Mech Engn Patiala 147004 Punjab India Meerut Inst Engn & Technol Dept Mech Engn Meerut Uttar Pradesh India

The aim of the current research is to compare the surface roughness models based on the Box-Behnken Design and levenberg-marquardt algorithm-based artificial neural networks. For prediction of the surface roughness of magnetic abrasive flow machining (MAFM) finished rare earth oxides (REOs) aluminium composites, Box-Behnken Design models were developed using three-level factorial design as magnetic flux density, number of cycles and extrusion pressure as process parameters. The artificial neural networks predictive models of surface roughness were developed using feed forward back propagation network procedures called the levenberg-marquardt algorithm. Also, an attempt has been made to compare the levenberg-marquardt algorithm-based artificial neural networks and Box-Behnken Design for the modeling of surface roughness results. The value of coefficient of determination for the Box-Behnken Design model is found to be high (R-2 = 0.9737), which is an indication of good fit for the model with high significance. The percentage error for the Box-Behnken Design model is observed to be more as compared to the levenberg-marquardt algorithm-based artificial neural networks model. The comparison evidently indicates that the prediction capabilities of trained artificial neural networks models are far better than the Box-Behnken Design models. Further, specimens were examined using atomic force microscopy for three-dimensional surface profiles.

关键词： Magnetic abrasive flow machining rare earth oxide surface roughness Box-Behnken Design levenberg-marquardt algorithm

来源：评论

学校读者我要写书评

暂无评论

Solving CNLS problems using levenberg-marquardt algorithm: A new fitting strategy combining limits and a symbolic Jacobian matrix

引用

JOURNAL OF ELECTROANALYTICAL CHEMISTRY 2020年 866卷

作者： Zic, Mark Subotic, Vanja Pereverzyev, Sergei Fajfar, Iztok Rudjer Boskovic Inst POB 180 Zagreb 10000 Croatia Graz Univ Technol Inst Thermal Engn Inffeldgasse 25b A-8010 Graz Austria Johann Radon Inst Computat & Appl Math Altenbergerstr 69 A-4040 Linz Austria Univ Ljubljana Fac Elect Engn Ljubljana 1000 Slovenia

The levenberg-marquardt algorithm (LMA) is generally used to solve diverse complex nonlinear least square (CNIS) problems and is one of the most used algorithms to extract equivalent electrochemical circuit (EEC) parameters from electrochemical impedance spectroscopy (EIS) data. It is a well-known fact that the convergence properties of the algorithm can be boosted by applying limits on EEC parameter values. However, when EEC parameter values are low (i.e., of the order of magnitude of 10(-4) or smaller), the applied limits increase the first derivatives approximation errors which occur when using a numerical Jacobian matrix. In this work, we discuss the importance of the Jacobian matrix in LMA and propose a design of a new EIS fitting engine. The new engine is based on a novel fitting scheme using limits and a symbolic Jacobian matrix instead of the numerical one, i.e. a strategy that has not yet been reported in any EIS study. We show that using a symbolic Jacobian matrix the algorithm convergence is superior to the one with a numerical Jacobian matrix. We also investigate how to improve poor convergence properties when we still have to use a numerical Jacobian matrix when analytic derivatives are not available. (C) 2020 Elsevier B.V. All rights reserved.

关键词： EIS CNLS Limits Symbolic Jacobian matrix levenberg-marquardt algorithm

来源：评论

学校读者我要写书评

暂无评论

Aerodynamic coefficients modeling using levenberg-marquardt algorithm and network

引用

AIRCRAFT ENGINEERING AND AEROSPACE TECHNOLOGY 2022年第3期94卷 336-350页

作者： Wang, Zhigang Li, Aijun Wang, Lihao Zhou, Xiangchen Wu, Boning Northwestern Polytech Univ Sch Automat Xian Peoples R China

Purpose The purpose of this paper is to propose a new aerodynamic parameter estimation methodology based on neural network and output error method, while the output error method is improved based on particle swarm algorithm. Design/methodology/approach Firstly, the algorithm approximates the dynamic characteristics of aircraft based on feedforward neural network. Neural network is trained by extreme learning machine, and the trained network can predict the aircraft response at (k + 1)th instant given the measured flight data at kth instant. Secondly, particle swarm optimization is used to enhance the convergence of levenberg-marquardt (LM) algorithm, and the improved LM method is used to substitute for the Gauss Newton algorithm in output error method. Finally, the trained neural network is combined with the improved output error method to estimate aerodynamic derivatives. Findings Neither depending on the initial guess of the parameters to be estimated nor requiring numerical integration of the aircraft motion equation, the proposed algorithm can be used for unstable aircraft and is successfully applied to extract aerodynamic derivatives from both simulated and real flight data. Research limitations/implications The proposed method requires iterative calculation and can only identify parameters offline. Practical implications The proposed method is successfully applied to estimate aircraft aerodynamic parameters and can also be used as a new algorithm for other optimization problems. Originality/value In this study, the output error method is improved to reduce the dependence on the initial value of parameters and expand its application scope. It is applied in aircraft aerodynamic parameter identification together with neural network.

关键词： Parameter estimation Particle swarm optimization Extreme learning machine Aerodynamic coefficients modeling levenberg-marquardt algorithm Output error method

来源：评论

学校读者我要写书评

暂无评论

Predictive control of seismic responses of a novel seismic isolation non-seismic isolation composite structure system based on levenberg-marquardt algorithm

引用

STRUCTURES 2023年 53卷 373-381页

作者： Huang, Kun Li, Jianhua Huzhou Vocat & Tech Coll Huzhou 313000 Peoples R China Huzhou Key Lab Green Bldg Technol Huzhou 313000 Peoples R China

In order to reduce the seismic responses of adjacent structures, this paper proposes a novel seismic isolation non-seismic isolation composite structure system, which is made up of a seismic isolation structure and a nonseismic isolation structure adjoined via controllers, and whose seismic responses are controlled with good effect by the principle of interacting control between the seismic isolation and non-seismic isolation structures. With a novel five-layer seismic isolation - non-seismic isolation composite structure system as an example, this paper builds a calculation model for the structure, derives the kinematic equations for the interacting control within this structural system, and adopts linear quadratic regulator (LQR) optimal control to analyze the control effect of this system and study the effect of time lag on the interacting control within this system. To address the issue of time lag in the interacting control between structures, an improved BP algorithm (levenberg-marquardt) is further adopted to set up a four-layer feedforward network to forecast the seismic responses of this novel composite structure system;the network structure is trained using the data acquired from LQR control under the action of the first 500 sampling points of the seismic motion taft, and the structure undergoes predictive control using the untrained seismic motion data of El Centro. The research results show that the interacting control of such a novel seismic isolation - non-seismic isolation composite structure system can achieve a good result, with the displacement response of the topmost layers of both structures decreasing by above 50 %;time lag may have an adverse effect on the control of the composite structure system, so that the structure's seismic response increases by 20 % similar to 40 %. The issue of time lag in interacting control can be addressed by adopting neutral network for predictive control, so that the structure's seismic responses are 11 % similar t

关键词： Novel composite structure system Seismic response levenberg-marquardt algorithm Predictive control

来源：评论

学校读者我要写书评

暂无评论

Non-linear calibration optimisation based on the levenberg-marquardt algorithm

引用

IET IMAGE PROCESSING 2020年第7期14卷 1402-1414页

作者： Hu, Guoliang Zhou, Zuofeng Cao, Jianzhong Huang, Huimin Xian Inst Opt & Precis Mech CAS Aircraft Opt Imaging & Measurement Technol Lab Xinxi Rd 17 Xian 710119 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

An outstanding calibration algorithm is the most important factor that affects the precision of attitude measurement. This study proposes a non-linear optimisation algorithm to refine the solutions of the initial guess obtained using the Zhang's technique, the Bouget's technique, or the Hartley's algorithm. Large sets of point correspondences were adopted to test the validity of the proposed method. Extensive practical experiments demonstrated that the proposed method can significantly improve the accuracy of calibration and ultimately obtains higher measurement precision. The error of the reprojection in the proposed method was <0.13 px. At a range of 1 m, the error rate was 0.5% for the length test and about 3% for the angle test. This study proposes a new method to calibrate the relationship between laser radar and the camera. Binocular vision was used to reconstruct the point cloud of the non-cooperative target. At the same time, data was also obtained using laser radar. Finally, the two groups of systems were fused. Accurate and dense three-dimensional information of the target was obtained. It could not only obtain the dense pose information of the target surface but also the texture and colour feature information of the target surface.

关键词： cameras calibration nonlinear programming optical radar nonlinear calibration optimisation levenberg-marquardt algorithm attitude measurement nonlinear optimisation algorithm laser radar point cloud Bouget technique Zhang technique Hartley algorithm calibration camera binocular vision

来源：评论

学校读者我要写书评

暂无评论

Fast Calculation Method for Continuous Power Flow of Microgrid Based on levenberg-marquardt algorithm

引用

IEEE ACCESS 2023年 11卷 49578-49586页

作者： Zhou, Yi Zhang, Junhong Gao, Wei Naval Univ Engn Sch Elect Engn Wuhan 430033 Peoples R China

There are possible non-convergence problems at load power critical value while using conventional continuous power flow methods. Generally, the step control method is used in the calculation to perform the calculation in small steps at the convergence to the load power critical value, and this method sacrifices the speed of the calculation although the calculation accuracy is high. In this paper, the levenberg-marquardt algorithm is used to search for the load power critical value firstly, and after that the step control algorithm is designed to perform fast power flow finding with known load power critical value, which takes into account the computational accuracy and computational efficiency. The case shows that the fast calculation method for continuous power flow of microgrid based on levenberg-marquardt algorithm proposed in this paper can guarantee the accuracy while effectively improving the calculation speed, and is suitable for online real-time evaluation of voltage stability and preventive control aided decision online calculation of isolated microgrid with high requirements for real-time.

关键词： Auxiliary factors two-step algorithm continuous power flow levenberg-marquardt algorithm microgrid

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：