检索结果-内蒙古大学图书馆

Cooperative Game Theory-Based Optimal Angular Momentum Management of Hybrid Attitude Control Actuator

IEEE ACCESS 2019年 7卷 6853-6865页

作者： Wu, Yunhua Zheng, Mohong He, Mengjie Zhang, Dawei He, Wei Hua, Bing Chen, Zhiming Wang, Feng Nanjing Univ Aeronaut & Astronaut Sch Astronaut Nanjing 210016 Jiangsu Peoples R China Harbin Inst Technol Sch Astronaut Harbin 150010 Heilongjiang Peoples R China CAST Inst Spacecraft Syst Engn Beijing 100094 Peoples R China Shanghai Electromech Engn Inst Shanghai 201109 Peoples R China

The next-generation space missions, such as the space moving target tracking mission and the agile attitude maneuvering mission and so on, propose a high requirement on spacecraft attitude control system. For such missions, hybrid attitude control actuators consisting of control moment gyro and reaction wheel, which can not only offer large control torque but also achieve high control precision, is the best alternative choice. For this hybrid actuator system, the angular momentum management is vital. To handle the momentum management problem, an optimal angular momentum strategy based on cooperative game theory is proposed. The cooperative game model is constructed according to the quadratic programming problem to achieve the minimization of control moment gyro gimbal angular speed and reaction wheel angular acceleration. The proposed cooperative game theory steering logic has overcome the control moment gyro singular problem and reaction wheel saturation problem of the hybrid system. In addition, the energy cost of the hybrid actuator system is reduced. Five groups of simulation scenarios are carried out to demonstrate the effectiveness of the proposed steering logic.

关键词： Agile attitude control hybrid actuator cooperative game theory quadratic programming problem angular momentum management

来源：评论

学校读者我要写书评

暂无评论

A structural information-based twin-hypersphere support vector machine classifier

引用

INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2017年第1期8卷 295-308页

作者： Peng, Xinjun Kong, Lingyan Chen, Dongjing Shanghai Normal Univ Dept Math Shanghai 200234 Peoples R China Sci Comp Key Lab Shanghai Univ Shanghai 200234 Peoples R China

Twin-hypersphere support vector machine (THSVM) for binary pattern recognition aims at generating two hyperspheres in the feature space such that each hypersphere contains as many as possible samples in one class and is as far as possible from the other one. THSVM has a fast learning speed since it solves two small sized support vector machine (SVM)-type quadratic programming problems (QPPs). However, it only simply considers the prior class-based structural information in the optimization problems. In this paper, a structural information-based THSVM (STHSVM) classifier for binary classification is presented. This proposed STHSVM focuses on the cluster-based structural information of the corresponding class in each optimization problem, which is vital for designing a good classifier in different real-world problems. In addition, it also leads to a fast learning speed since this STHSVM solves a series of smaller-sized QPPs compared with THSVM. Experimental results demonstrate that STHSVM is superior in generalization performance to other classifiers.

关键词： Binary classification quadratic programming problem Twin-hypersphere support vector machine Structural information

来源：评论

学校读者我要写书评

暂无评论

Unconstrained convex minimization based implicit Lagrangian twin extreme learning machine for classification (ULTELMC)

引用

APPLIED INTELLIGENCE 2020年第4期50卷 1327-1344页

作者： Borah, Parashjyoti Gupta, Deepak Natl Inst Technol Dept Comp Sci & Engn Yupia Arunachal Prade India

The recently proposed twin extreme learning machine (TELM) requires solving two quadratic programming problems (QPPs) in order to find two non-parallel hypersurfaces in the feature that brings in the additional requirement of external optimization toolbox such as MOSEK. In this paper, we propose implicit Lagrangian TELM for classification via unconstrained convex minimization problem (ULTELMC) and further suggest iterative convergent schemes which eliminates the requirement of external optimization toolbox generally required in solving the quadratic programming problems (QPPs) of TELM. The solutions to the dual variables of the proposed ULTELMC are obtained using iterative schemes containing 'plus' function which is not differentiable. To overcome this shortcoming, the generalized derivative approach and smooth approximation approaches are suggested. Further, to test the performance of the proposed approaches, classification performances are compared with support vector machine (SVM), twin support vector machine (TWSVM), extreme learning machine (ELM), twin extreme learning machine (TELM) and Lagrangian extreme learning machine (LELM). Moreover, non-requirement to solve QPPs makes the iterative schemes find the solution faster as compared to the reported methods that finds the solution in dual space. Computational times required in finding the solutions are also presented for comparison.

关键词： Extreme learning machine Unconstrained minimization Smoothing approaches quadratic programming problem Iterative schemes

来源：评论

学校读者我要写书评

暂无评论

Model predictive control for deeply pipelined field-programmable gate array implementation: algorithms and circuitry

引用

IET CONTROL THEORY AND APPLICATIONS 2012年第8期6卷 1029-1041页

作者： Jerez, J. L. Ling, K. -V. Constantinides, G. A. Kerrigan, E. C. Univ London Imperial Coll Sci Technol & Med Dept Elect & Elect Engn London SW7 2AZ England Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Univ London Imperial Coll Sci Technol & Med Dept Aeronaut London SW7 2AZ England

Model predictive control (MPC) is an optimisation-based scheme that imposes a real-time constraint on computing the solution of a quadratic programming (QP) problem. The implementation of MPC in fast embedded systems presents new technological challenges. In this paper we present a parameterised field-programmable gate array implementation of a customised QP solver for optimal control of linear processes with constraints, which can achieve substantial acceleration over a general purpose microprocessor, especially as the size of the optimisation problem grows. The focus is on exploiting the structure and accelerating the computational bottleneck in a primal-dual interior-point method. We then introduce a new MPC formulation that can take advantage of the novel computational opportunities, in the form of parallel computational channels, offered by the proposed pipelined architecture to improve performance even further. This highlights the importance of the interaction between the control theory and digital system design communities for the success of MPC in fast embedded systems.

关键词： quadratic programming problem optimisation-based scheme predictive control Logic and switching circuits pipeline processing digital system design Multiprocessing systems deeply pipelined field-programmable gate array implementation pipelined architecture linear processes field programmable gate arrays parallel computational channels general purpose microprocessor model predictive control Parallel architecture quadratic programming primal-dual interior-point method Optimal control fast embedded systems customised QP solver real-time constraint optimal control

来源：评论

学校读者我要写书评

暂无评论

Functional iterative approaches for solving support vector classification problems based on generalized Huber loss

引用

NEURAL COMPUTING & APPLICATIONS 2020年第13期32卷 9245-9265页

作者： Borah, Parashjyoti Gupta, Deepak Natl Inst Technol Arunachal Pradesh Dept Comp Sci & Engn Yupia India

Classical support vector machine (SVM) and its twin variant twin support vector machine (TWSVM) utilize the Hinge loss that shows linear behaviour, whereas the least squares version of SVM (LSSVM) and twin least squares support vector machine (LSTSVM) uses L-2-norm of error which shows quadratic growth. The robust Huber loss function is considered as the generalization of Hinge loss and L-2-norm loss that behaves like the quadratic L-2-norm loss for closer error points and the linear Hinge loss after a specified distance. Three functional iterative approaches based on generalized Huber loss function are proposed in this paper to solve support vector classification problems of which one is based on SVM, i.e. generalized Huber support vector machine and the other two are in the spirit of TWSVM, namely generalized Huber twin support vector machine and regularization on generalized Huber twin support vector machine. The proposed approaches iteratively find the solutions and eliminate the requirements to solve any quadratic programming problem (QPP) as for SVM and TWSVM. The main advantages of the proposed approach are: firstly, utilize the robust Huber loss function for better generalization and for lesser sensitivity towards noise and outliers as compared to quadratic loss;secondly, it uses functional iterative scheme to find the solution that eliminates the need to solving QPP and also makes the proposed approaches faster. The efficacy of the proposed approach is established by performing numerical experiments on several real-world datasets and comparing the result with related methods, viz. SVM, TWSVM, LSSVM and LSTSVM. The classification results are convincing.

关键词： Support vector machine Functional iterative scheme Huber loss function quadratic programming problem

来源：评论

学校读者我要写书评

暂无评论

Computationally efficient model predictive control for a class of linear parameter-varying systems

引用

IET CONTROL THEORY AND APPLICATIONS 2018年第10期12卷 1384-1392页

作者： Cavanini, Luca Cimini, Gionata Ippoliti, Gianluca Univ Politecn Marche Dipartimento Ingn Informaz Via Brecce Bianche 12 I-60131 Ancona Italy ODYS Srl Milan Italy

The use of linear parameter varying (LPV) prediction models has been proven to be an effective solution to develop model predictive control (MPC) algorithms for linear and non-linear systems. However, the computational effort is a crucial issue for LPV-MPC, which has severely limited its application especially in embedded control. Indeed, for dynamical systems of dimension commonly found in embedded applications, the time needed to form the quadratic programming (QP) problem at each time step, can be substantially higher than the average time to solve it, making the approach infeasible in many control boards. This study presents an algorithm that drastically reduces this computational complexity for a particular class of LPV systems. They show that when the input matrix is right-invertible, the rebuild phase of the QP problem can be accelerated by means of a coordinate transformation which approximates the original formulation. Then they introduce a variant of the algorithm, able to further reduce this time, at the cost of a slightly increased sub-optimality. The presented results on vehicle dynamics and electrical motor control confirm the effectiveness of the two novel methods, especially in those applications where computational load is a key indicator for success.

关键词： time-varying systems vehicle dynamics machine control quadratic programming linear systems computational complexity control system synthesis predictive control electric motors effective solution model predictive control algorithms computational effort crucial issue LPV-MPC embedded control embedded applications quadratic programming problem average time control boards computational complexity LPV systems QP problem electrical motor control computational load linear parameter-varying systems computationally efficient model predictive control LPV prediction models vehicle dynamics

来源：评论

学校读者我要写书评

暂无评论

A mathematical programming model for computing the Fries number of a fullerene

引用

APPLIED MATHEMATICAL MODELLING 2015年第18期39卷 5473-5479页

作者： Salami, M. Ahmadi, M. B. Shiraz Univ Dept Math Coll Sci Shiraz Iran

A fullerene graph is a cubic 3-connected plane graph with pentagonal and hexagonal faces. The Fries number of a fullerene is the maximum number of benzene-like faces over all possible perfect matchings. The Fries number and its associated Kekule structure of a fullerene play a key role in molecular energy and stability. In this paper we propose a binary integer linear programming and a quadratic programming model for determining the Fries number of a fullerene. Moreover, interior point approach, as one of the most robust optimization techniques, is implemented to find the optimal solution of the proposed quadratic programming problem in moderate computing time. (C) 2015 Elsevier Inc. All rights reserved.

关键词： Binary integer linear programming quadratic programming problem Perfect matching Fries number Fullerenes

来源：评论

学校读者我要写书评

暂无评论

An improved constrained dynamic matrix control for temperature in an industrial coke furnace

引用

MEASUREMENT & CONTROL 2019年第5-6期52卷 409-417页

作者： Zou, Hongbo Wang, Limin Hangzhou Dianzi Univ Inst Informat & Control Hangzhou 310018 Zhejiang Peoples R China Hainan Normal Univ Sch Math & Stat Haikou Hainan Peoples R China

In order to derive the feasible control law of the constrained model predictive control scheme, quadratic programming has been introduced as an effective method. It is known that the typical performance index for model predictive control strategies under various constraints can be converted into a standard quadratic programming problem;however, there may be no feasible solutions for the corresponding quadratic programming problem when the working conditions are too bad or constraints are too rigorous, the real-time control law cannot be updated and the system performance may be deteriorated. To cope with such problems, an improved quadratic programming problem in which relaxations are employed to increase the possibility of successful solutions is proposed for the constrained dynamic matrix control approach in this paper. By adopting the introduced relaxations, more degrees of relaxations are provided for the optimization process under the case of over-constrained, such that the control law is easier to yield. Case study on the temperature regulation of the coke furnace demonstrates the validity of the improved quadratic programming structure-based dynamic matrix control strategy. Simulation results show that the proposed scheme yields improved control performance.

关键词： Dynamic matrix control quadratic programming problem relaxations constraints temperature regulation

来源：评论

学校读者我要写书评

暂无评论

Collocation-variation difference schemes with several collocation points for differential-algebraic equations

引用

APPLIED NUMERICAL MATHEMATICS 2020年 149卷 153-163页

作者： Bulatov, Mikhail Solovarova, Liubov RAS SB Matrosov Inst Syst Dynam & Control Theory Lermontov St 134 Irkutsk Russia

We consider the initial value problem in linear differential-algebraic equations. We propose collocation-variation difference schemes with several collocation points and show the principal difference between these approaches and the known difference schemes. Analysis of the particular cases of such schemes and calculations of the test examples are given. (C) 2019 IMACS. Published by Elsevier B.V. All rights reserved.

关键词： Differential-algebraic equations Difference scheme quadratic programming problem Initial value problem

来源：评论

学校读者我要写书评

暂无评论

Mean-variance optimal trading problem subject to stochastic dominance constraints with second order autoregressive price dynamics

引用

MATHEMATICAL METHODS OF OPERATIONS RESEARCH 2017年第1期86卷 29-69页

作者： Singh, Arti Selvamuthu, Dharmaraja IIT Delhi Dept Math New Delhi 110016 India

The efficient modeling of execution price path of an asset to be traded is an important aspect of the optimal trading problem. In this paper an execution price path based on the second order autoregressive process is proposed. The proposed price path is a generalization of the existing first order autoregressive price path in literature. Using dynamic programming method the analytical closed form solution of unconstrained optimal trading problem under the second order autoregressive process is derived. However in order to incorporate non-negativity constraints in the problem formulation, the optimal static trading problems under second order autoregressive price process are formulated. For a risk neutral investor, the optimal static trading problem of minimizing expected execution cost subject to non-negativity constraints is formulated as a quadratic programming problem. Whereas, for a risk averse investor the variance of execution cost is considered as a measure for the timing risk, and the mean-variance problem is formulated. Moreover, the optimal static trading problem subject to stochastic dominance constraints with mean-variance static trading strategy as the reference strategy is studied. Using Static approximation method the algorithm to solve proposed optimal static trading problems is presented. With numerical illustrations conducted on simulated data and the real market data, the significance of second order autoregressive price path, and the optimal static trading problems is presented.

关键词： Optimal trading Execution cost Stochastic dominance Autoregressive behavior quadratic programming problem Risk averse

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：