检索结果-内蒙古大学图书馆

Novel artificial neural network with simulation aspects for solving linear and quadratic programming problems

COMPUTERS & MATHEMATICS WITH APPLICATIONS 2007年第9期53卷 1439-1454页

作者： Ghasabi-Oskoei, H. Malek, A. Ahmadi, A. Tarbiat Modares Univ Acad Ctr Educ Culture & Res Math & Informat Res Grp Tehran Iran Tarbiat Modares Univ Dept Math Tehran Iran

In this paper linear and quadratic programming problems are solved using a novel recurrent artificial neural network. The new model is simpler and converges very fast to the exact primal and dual solutions simultaneously. The model is based on a nonlinear dynamical system, using arbitrary initial conditions. In order to construct an economy model, here we avoid using analog multipliers. The dynamical system is a time dependent system of equations with the gradient of specific Lyapunov energy function in the right hand side. Block diagram of the proposed neural network model is given. Fourth order Runge-Kutta method with controlled step size is used to solve the problem numerically. Global convergence of the new model is proved, both theoretically and numerically. Numerical simulations show the fast convergence of the new model for the problems with a unique solution or infinitely many. This model converges to the exact solution independent of the way that we may choose the starting points, i.e. inside, outside or on the boundaries of the feasible region. (c) 2007 Elsevier Ltd. All rights reserved.

关键词： artificial neural networks dynamical systems quadratic programming linear programming global convergence

来源：评论

学校读者我要写书评

暂无评论

Efficient redundancy reduced subgroup discovery via quadratic programming

引用

JOURNAL OF INTELLIGENT INFORMATION SYSTEMS 2015年第2期44卷 271-288页

作者： Li, Rui Perneczky, Robert Drzezga, Alexander Kramer, Stefan Tech Univ Munich Inst Informat I12 D-85748 Garching Germany Univ London Imperial Coll Sci Technol & Med Neuroepidemiol & Ageing Res Unit Sch Publ Hlth Fac Med London W6 8RP England Tech Univ Munich Klin & Poliklin Psychiat & Psychotherapie D-81675 Munich Germany West London Mental Hlth Trust West London Cognit Disorders Treatment & Res Unit London TW8 8DS England Univ Cologne Nukl Med Klin & Poliklin D-50937 Cologne Germany Johannes Gutenberg Univ Mainz Inst Informat D-55128 Mainz Germany

Subgroup discovery is a task at the intersection of predictive and descriptive induction, aiming at identifying subgroups that have the most unusual statistical (distributional) characteristics with respect to a property of interest. Although a great deal of work has been devoted to the topic, one remaining problem concerns the redundancy of subgroup descriptions, which often effectively convey very similar information. In this paper, we propose a quadratic programming based approach to reduce the amount of redundancy in the subgroup rules. Experimental results on 12 datasets show that the resulting subgroups are in fact less redundant compared to standard methods. In addition, our experiments show that the computational costs are significantly lower than the costs of other methods compared in the paper.

关键词： Subgroup discovery Mutual information quadratic programming Rule learning Redundancy

来源：评论

学校读者我要写书评

暂无评论

A Penalty Strategy Combined Varying-Parameter Recurrent Neural Network for Solving Time-Varying Multi-Type Constrained quadratic programming Problems

引用

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2021年第7期32卷 2993-3004页

作者： Zhang, Zhijun Yang, Song Zheng, Lunan South China Univ Technol Sch Automat Sci & Engn Guangzhou 510640 Peoples R China

To obtain the optimal solution to the time-varying quadratic programming (TVQP) problem with equality and multitype inequality constraints, a penalty strategy combined varying-parameter recurrent neural network (PS-VP-RNN) for solving TVQP problems is proposed and analyzed. By using a novel penalty function designed in this article, the inequality constraint of the TVQP can be transformed into a penalty term that is added into the objective function of TVQP problems. Then, based on the design method of VP-RNN, a PS-VP-RNN is designed and analyzed for solving the TVQP with penalty term. One of the greatest advantages of PS-VP-RNN is that it cannot only solve the TVQP with equality constraints but can also solve the TVQP with inequality and bounded constraints. The global convergence theorem of PS-VP-RNN is presented and proved. Finally, three numerical simulation experiments with different forms of inequality and bounded constraints verify the effectiveness and accuracy of PS-VP-RNN in solving the TVQP problems.

关键词： Recurrent neural networks Real-time systems Convergence Linear matrix inequalities quadratic programming Multitype inequality constraint penalty function quadratic programming (QP) recurrent neural network (RNN) time varying

来源：评论

学校读者我要写书评

暂无评论

A Predefined Fixed-Time Convergence ZNN and Its Applications to Time-Varying quadratic programming Solving and Dual-Arm Manipulator Cooperative Trajectory Tracking

引用

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2023年第8期19卷 8691-8702页

作者： Jin, Jie Chen, Weijie Chen, Chaoyang Chen, Long Tang, Zhijun Chen, Lei Wu, Lianghong Zhu, Changren Hunan Univ Sci & Technol Sch Informat & Elect Engn Xiangtan 411201 Peoples R China Changsha Med Univ Sch Informat Engn Changsha 410219 Peoples R China

The zeroing neural network (ZNN) model, a powerful approach for addressing time-varying problems, has been extensively applied in the calculation and optimization fields. In this article, a new pattern activation function, the power piecewise activation function (PPAF), is proposed to establish a predefined fixed-time convergent ZNN (PFTZNN) for finding solutions to the time-varying quadratic programming problem. In comparison with the traditional activation functions, multisegmentation is a remarkable feature of the PPAF;consequently, the advantage of PPAF is that its parameters can be flexibly adjusted according to actual needs. Specifically, because of the multisegment characteristics of the PPAF, the convergence speed of the PPAF-activated PFTZNN model can be flexibly adjusted based on distinct requirements. The fixed-time convergence property of the PPAF-activated PFTZNN model is validated by detailed mathematical theoretical analysis, and its upper bound convergence time is directly calculated. Then, the comparative simulation results of the PPAF-activated PFTZNN model with other existing ZNN models for time-varying quadratic programming are provided for the further verification of its superior convergence speed and robustness. In addition, the proposed PFTZNN model is applied for dual-arm manipulator cooperative trajectory tracking, and its practical application ability is demonstrated by united simulation experiments of MATLAB and Robot studio. Finally, the PFTZNN model is also applied to control a real dual-arm manipulator to complete the trajectory tracking task, which further validates its superior performance together and widespread applicability.

关键词： Mathematical models Convergence Manipulators Trajectory tracking Numerical models quadratic programming Recurrent neural networks Activation function dual-arm manipulator fixed-time convergence time-varying quadratic programming (TVQP) zeroing neural network (ZNN)

来源：评论

学校读者我要写书评

暂无评论

Five-instant type discrete-time ZND solving discrete time-varying linear system, division and quadratic programming

引用

NEUROCOMPUTING 2019年 331卷 323-335页

作者： Li, Jian Zhang, Yunong Mao, Mingzhi Sun Yat Sen Univ Sch Informat Sci & Technol Guangzhou 510006 Guangdong Peoples R China Minist Educ Key Lab Machine Intelligence & Adv Comp Guangzhou 510006 Guangdong Peoples R China

Discrete time-varying linear system (LS) is a fundamental topic in science and engineering. However, conventional methods essentially designed for time-invariant LS generally assume that LS is time-invariant during a small time interval (i.e., sampling gap) for solving time-varying LS. This assumption quite limits their precision because of the existing of lagging errors. Discarding this assumption, Zhang neural dynamics (ZND) method improves the precision for LS solving, which is a great alternative for the solving of discrete time-varying problems. Note that precision solutions to discrete time-varying problems depend on discretization formulas. In this paper, we propose a new ZND model to solve the discrete time-varying LS. The discrete time-varying division is a special case of discrete time-varying LS with the solution being a scalar while it is usually studied alone. Considering the above inner connection, we further propose a special model for solving the discrete time-varying division. Moreover, as an application of discrete time-varying LS, the discrete time-varying quadratic programming (QP) subject to LS is also studied. The convergence and precision of proposed models are guaranteed by theoretical analyses and substantiated by numerous numerical experiments. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Discretization formula Discrete-time Zhang neural dynamics Discrete time-varying linear system Division quadratic programming

来源：评论

学校读者我要写书评

暂无评论

Interval regression analysis by quadratic programming approach

引用

IEEE TRANSACTIONS ON FUZZY SYSTEMS 1998年第4期6卷 473-481页

作者： Tanaka, H Lee, H Osaka Prefecture Univ Dept Ind Engn Osaka 5998531 Japan

When we use linear programming in possibilistic regression analysis, some coefficients tend to become crisp because of the characteristic of linear programming. On the other hand, a quadratic programming approach gives more diverse spread coefficients than a linear programming one. Therefore, to overcome the crisp characteristic of linear programming, we propose interval regression analysis based on a quadratic programming approach. Another advantage of adopting a quadratic programming approach in interval regression analysis is to be able to integrate both the property of central tendency in least squares and the possibilistic property in fuzzy regression, By changing the weights of the quadratic function, we can analyze the given data from different viewpoints. For data with crisp inputs and interval outputs, the possibility and necessity models can be considered. Therefore, the unified quadratic programming approach obtaining the possibility and necessity regression models simultaneously is proposed. Even though there always exist possibility estimation models, the existence of necessity estimation models is not guaranteed if we fail to assume a proper function fitting to the given data as a regression model. Thus, we consider polynomials as regression models since any curve can be represented by the polynomial approximation, Using polynomials, we discuss how to obtain approximation models which fit well to the given data where the measure of fitness is newly defined to gauge the similarity between the possibility and the necessity models. Furthermore, from the obtained possibility and necessity regression models, a trapezoidal fuzzy output can be constructed.

关键词： interval regression analysis polynomials possibility and necessity models quadratic programming

来源：评论

学校读者我要写书评

暂无评论

A Time-Specified Zeroing Neural Network for quadratic programming With Its Redundant Manipulator Application

引用

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2022年第5期69卷 4977-4987页

作者： Kong, Ying Jiang, Yunliang Li, Xianyi Lei, Jingsheng Zhejiang Univ Sci & Technol Dept Informat & Elect Engn Hangzhou 310023 Peoples R China Huzhou Univ Sch Informat Engn Huzhou 313000 Peoples R China Zhejiang Univ Sci & Technol Sch Sci Hangzhou 310023 Peoples R China

In this article, to solve a time-varying quadratic programming with equation constraint, a new time-specified zeroing neural network (TSZNN) is proposed and analyzed. Unlike the existing methods such as the Zhang neural network with different activation functions and a finite-time neural network, the TSZNN model is incorporated into a terminal attractor, and the convergent error can be guaranteed to reduce to zero in advance (instead of the finite-time property). The main advantage of the TSZNN model is that it is independent of the initial state of the systematic dynamics, which is much astonishing to the finite convergence relying on the initial conditions and comprehensively modifies the convergent performance. Mathematical analyses substantiate the prespecified convergence of the TSZNN model and high convergent precision under the situation of various convergent time settings. The prespecified convergence of the TSZNN model for a quadratic programming problem has been mathematically proved under different convergent constant settings. In addition, simulation applications conducted on a repeatable trajectory planning of the redundant manipulator are studied to demonstrate the validity of the proposed TSZNN model.

关键词： Mathematical model Neural networks Manipulators Convergence Kinematics quadratic programming Optimization Kinematic repetitive control prespecified time time-specified zeroing neural network (TSZNN)

来源：评论

学校读者我要写书评

暂无评论

A semi-smooth Newton method for a special piecewise linear system with application to positively constrained convex quadratic programming

引用

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS 2016年 301卷 91-100页

作者： Barrios, J. G. Bello Cruz, J. Y. Ferreira, O. P. Nemeth, S. Z. IME UFG Ave Esperana S-NCampus Samambaia BR-74690900 Goiania Go Brazil Univ Birmingham Sch Math Watson Bldg Birmingham B15 2TT W Midlands England

In this paper a special piecewise linear system is studied. It is shown that, under a mild assumption, the semi-smooth Newton method applied to this system is well defined and the method generates a sequence that converges linearly to a solution. Besides, we also show that the generated sequence is bounded, for any starting point, and a formula for any accumulation point of this sequence is presented. As an application, we study the convex quadratic programming problem under positive constraints. The numerical results suggest that the semi-smooth Newton method achieves accurate solutions to large scale problems in few iterations. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Piecewise linear system quadratic programming Convex set Convex cone Semi-smooth Newton method

来源：评论

学校读者我要写书评

暂无评论

Neural network models and its application for solving linear and quadratic programming problems

引用

APPLIED MATHEMATICS AND COMPUTATION 2006年第1期172卷 305-331页

作者： Effati, S Nazemi, AR Teacher Training Univ Sabzevar Dept Math Sabzevar Iran

In this paper we consider two recurrent neural network model for solving linear and quadratic programming problems. The first model is derived from an unconstraint minimization reformulation of the program. The second model directly is obtained of optimality condition for an optimization problem. By applying the energy function and the duality gap, we will compare the convergence these models. We also explore the existence and the convergence of the trajectory and stability properties for the neural networks models. Finally, in some numerical examples, the effectiveness of the methods is shown. (c) 2005 Elsevier Inc. All rights reserved.

关键词： neural network linear programming quadratic programming reformulation optimality condition convergence

来源：评论

学校读者我要写书评

暂无评论

A Regularized and Smoothed Fischer-Burmeister Method for quadratic programming With Applications to Model Predictive Control

引用

IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2019年第7期64卷 2937-2944页

作者： Liao-McPherson, Dominic Huang, Mike Kolmanovsky, Ilya Univ Michigan Ann Arbor MI 48109 USA Toyota Motors North Amer R&D Ann Arbor MI 48105 USA

This paper considers solving convex quadratic programs in a real-time setting using a regularized and smoothed Fischer-Burmeister method (FBRS). The Fischer-Burmeister function is used to map the optimality conditions of a quadratic program to a nonlinear system of equations which is solved using Newton's method. Regularization and smoothing are applied to improve the practical performance of the algorithm and a merit function is used to globalize convergence. FBRS is simple to code, easy to warmstart, robust to early termination, and has attractive theoretical properties. making it appealing for real time and embedded applications. Numerical experiments using several predictive control examples show that the proposed method is competitive with other state-of-the-art solvers.

关键词： Convex optimization embedded optimization model predictive control (MPC) Newton's method nonsmooth analysis quadratic programming semismooth

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：