实时求解线性规划问题的原对偶神经网络(英文)

本文探讨了线性规划的原问题与对偶问题理论,并在此基础上可开发出一种用于在线求解线性规划的递归神经网络和应用于冗余机器手臂逆运动学的求解问题上.如,Tang等人开展的原对偶神经网络.但鉴于对偶理论的复杂性和多样性,该原对偶神经网络模型仅可以得到线性规划问题的可行解,而本文对该网络模型改进后可得到线性规划问题的最优解.仿真结果证实了这种改进模型在解决线性规划问题上的有效性、正确性和高效率.     

运动员跳台跳水运动学分析

为探究运动员跳水完成动作时间与体型的关系,根据角动量守恒原理建立了跳台跳水的运动学模型.首先利用欧拉角描述人体的姿态参数,随后结合旋转矩阵推导各姿态角速度之间的关系;将人体分为9部分,分别计算各动作下的转动惯量;利用微元分析法求解人体姿态参数进而得到运动员完成翻腾转体等动作的完成时间.该运动学模型的建立与分析为跳水动作难度系数的设置提供了参考,为具有腾空类体育动作的运动学参数计算提供了方法.     

信息熵在优化问题中的应用

一、引言一般实际优化问题总包含大量约束,使求解非常困难。若直接求解这一问题,会消耗大量机时和占用大量存贮,甚至使求解变为不可能。许多优化算法,用所谓“紧约束集合”策略,来缓解这一困难。即在每次迭代中,按某种方式选取少量约束作为紧约束集,参加迭代运算;然后按照迭代所得解的信息,修改紧约束集,重新求解修改了的问题,直至收敛。这种做法虽然缓解了大型问题求解的困难,但方法本身带有很大程度的主观性和任意性。一般     

单侧接触问题的拟有效集方法

单侧接触问题可以模型化为一个带不等式约束的数学规划问题。针对不等式约束问题求解的困难,提出了一个拟有效集方法。在每次迭代中,先利用上次迭代得到的解将问题转化为一个无接触问题,然后以其解作为当前迭代的初始解,且在每次迭代里可以同时更换一组接触点对,而不是象Lemke方法那样每次迭代仅更换一个接触点对。因而,该算法极大地提高了求解效率,算例表明了该算法的高效性和可靠性。     

一种七自由度冗余机械臂的逆运动学优化算法

在建立一种常见的七自由度冗余机械臂D-H模型的基础上,结合固定关节角法和加权最小范数法,提出了一种基于二次计算的逆运动学优化算法.基于加权最小范数法具有回避关节极限以得到优化解的优势,该算法一方面在牺牲一定时间复杂度的条件下进一步提高了逆运动学求解的精度,另一方面基于迭代算法解决了雅克比伪逆不存在时加权最小范数法无法求解的问题.通过仿真结果可以看出,基于加权最小范数法的二次计算法在对期望轨迹的跟踪精度上有了很大提高,并能够较好地解决雅克比伪逆不存在时的求解问题.     

求解线性约束的区间二次规划问题的神经网络

在本文中,基于神经网络,提出了一类求解具有线性约束区间二次规划问题的方法,使用增广拉格朗日函数,建立了求解规划问题的神经网络模型。基于压缩不动点理论,证明了所提出神经网络的平衡点就是等式约束区间二次规划问题的最优解。使用适当的Lyapunov函数,证明了所提出的神经网络的平衡点是全局指数稳定的。最后,两个数值仿真结果验证了本文所用方法的可行性与有效性。     

基于近似一阶信息的加速的bundle level算法

本文提出了四种加速的BL(bundle level)算法来分别求解凸光滑函数、强凸光滑函数的极小值问题和一类鞍点(saddle-point)问题.这些算法可以运用目标函数的近似的一阶信息来得到上述几类问题的近似解.本文重点研究了在一阶信息误差上界可自由选取和给定不变的两种情形下,所提出的算法中近似解能达到的最佳精度以及相应的迭代复杂度.     

考虑再利用需求的垃圾回收处理优化模型

垃圾回收处理是建设资源节约和环境友好形社会(即两型社会)的基本要求.本文利用逆向物流管理优化方法研究带确定性日回收垃圾处理问题,对经过垃圾处理中心处理过后的垃圾分全部或部分可被再利用两种情形,建立了垃圾回收相关企业成本最小的约束优化模型.该模型是0-1整数规划模型,能够直接在LINGO软件平台上求解.应用实例验证了模型的效用.     

二层规划的神经网络解法

本文研究了基于神经网络的二层规划问题.利用互补松弛条件的扰动,获得了二层规划问题局部最优解的充分条件,克服了互补松弛条件不满足约束规格的局限性,并给出了相应的神经网络求解方法,从而求解原二层规划问题,数值实验表明算法有效.     

不等式约束优化全局收敛的滤子线性方程组算法

设计了求解不等式约束非线性规划问题的一种新的滤子序列线性方程组算法,该算法每步迭代由减小约束违反度和目标函数值两部分构成.利用约束函数在某个中介点线性化的方法产生搜索方向.每步迭代仅需求解两个线性方程组,计算量较小.在一般条件下,证明了算法产生的无穷迭代点列所有聚点都是可行点并且所有聚点都是所求解问题的KKT点.     

Improving the inverse kinematics reconstruction of human movement from motion capture data

Marker based motion capture methods are well known techniques for the acquisition of human motion. For the kinematic and dynamic analysis the recorded data is usually used to drive a rigid body model of the human body. Skin artifacts, which are caused by skin deformation and displacement of markers with respect to the underlying bone, are regarded as the most critical source of error in the inverse kinematics reconstruction of human movement. State-of-the-art algorithms use optimization and multibody models with joint constraints in order to overcome these effects. This work presents an optimization based inverse kinematics approach, which is able to adapt the model kinematics subject-specifically and to compute the time trajectories of kinematic variables from marker data including velocity and acceleration. (© 2010 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Solución de la cinemática directa de la plataforma Gough-Stewart general usando un algoritmo híbrido de optimización

The direct kinematics problem for parallel robots can be stated as follows: given values of the joint variables, the corresponding Cartesian variable values, the pose of the end-effector, must be found. Most of the times the direct kinematics problem involves the solution of a system of non-linear equations. The most efficient methods to solve such kind of equations assume convexity in a cost function which minimum is the solution of the non-linear system. In consequence, the capacity of such methods depends on the knowledge about an starting point which neighboring region is convex, hence the method can find the global minimum. This article propose a method based on probabilistic learning about an adequate starting point for the Dogleg method which assumes local convexity of the function. The proposed method efficiently avoids the local minima, without need of human intervention or apriori knowledge, thus it shows a more robust performance than the simple Dogleg method or other gradient based methods. To demonstrate the performance of the proposed hybrid method, numerical experiments and the respective discussion are presented. The proposal can be extended to other structures of closed-kinematics chains, to the general solution of systems of non-linear equations, and to the minimization of non-linear functions.     

Kinematics of an offset 3-UPU translational parallel manipulator by the homotopy continuation method

For most parallel manipulators, the inverse kinematics is straightforward, while the direct kinematics is challenging. The latter requires the solution of a system of nonlinear equations. In this paper we use the homotopy continuation method to solve the forward and inverse kinematic problems of an offset 3-UPU translational parallel manipulator. The homotopy continuation method is a novel method which alleviates drawbacks of the traditional numerical techniques, namely; the acquirement of good initial guess values, the problem of convergence and computing time. The direct kinematics problem of the manipulator leads to 16 real solutions.     

Two New Mixture Models Related to the Inverse Gaussian Distribution

This article presents a new family of logarithmic distributions to be called the sinh mixture inverse Gaussian model and its associated life distribution referred as the extended mixture inverse Gaussian model. Specifically, the density, distribution function, and moments are developed for the sinh mixture inverse Gaussian distribution. Next, the extended mixture inverse Gaussian distribution is characterized. A graphical analysis of the densities of the new models is also provided. In addition, a lifetime analysis is presented for the extended mixture inverse Gaussian distribution. Finally, an example with a real data set is given to illustrate the methodology, which indicates that the new models result in a better fit to the data than some other well-known distributions.     

Robust regression for mixed Poisson–Gaussian model

This paper focuses on efficient computational approaches to compute approximate solutions of a linear inverse problem that is contaminated with mixed Poisson–Gaussian noise, and when there are additional outliers in the measured data. The Poisson–Gaussian noise leads to a weighted minimization problem, with solution-dependent weights. To address outliers, the standard least squares fit-to-data metric is replaced by the Talwar robust regression function. Convexity, regularization parameter selection schemes, and incorporation of non-negative constraints are investigated. A projected Newton algorithm is used to solve the resulting constrained optimization problem, and a preconditioner is proposed to accelerate conjugate gradient Hessian solves. Numerical experiments on problems from image deblurring illustrate the effectiveness of the methods.     

Constrained numerical optimization methods for blind deconvolution

This paper describes a nonlinear least squares framework to solve a separable nonlinear ill-posed inverse problem that arises in blind deconvolution. It is shown that with proper constraints and well chosen regularization parameters, it is possible to obtain an objective function that is fairly well behaved and the nonlinear minimization problem can be effectively solved by a Gauss–Newton method. Although uncertainties in the data and inaccuracies of linear solvers make it unlikely to obtain a smooth and convex objective function, it is shown that implicit filtering optimization methods can be used to avoid becoming trapped in local minima. Computational considerations, such as computing the Jacobian, are discussed, and numerical experiments are used to illustrate the behavior of the algorithms. Although the focus of the paper is on blind deconvolution, the general mathematical model addressed in this paper, and the approaches discussed to solve it, arise in many other applications.     

Discrete-analytic schemes for solving an inverse coefficient heat conduction problem in a layered medium with gradient methods

A method for constructing numerical schemes for an inverse coefficient heat conduction problem with boundary measurement data and piecewise-constant coefficients is considered. Some numerical schemes for a gradient optimization algorithm to solve the inverse problem are presented. The method is based on locally-adjoint problems in combination with approximation methods in Hilbert spaces.     

Fractional normal inverse Gaussian diffusion

A fractional normal inverse Gaussian (FNIG) process is a fractional Brownian motion subordinated to an inverse Gaussian process. This paper shows how the FNIG process emerges naturally as the limit of a random walk with correlated jumps separated by i.i.d. waiting times. Similarly, we show that the NIG process, a Brownian motion subordinated to an inverse Gaussian process, is the limit of a random walk with uncorrelated jumps separated by i.i.d. waiting times. The FNIG process is also derived as the limit of a fractional ARIMA processes. Finally, the NIG densities are shown to solve the relativistic diffusion equation from statistical physics.     

A new approach to the incorporation of servo constraints in multibody dynamics

A new index reduction approach is developed to solve the servo constraint problems [2] in the inverse dynamics simulation of underactuated mechanical systems. The servo constraint problem of underactuated systems is governed by differential algebraic equations (DAEs) with high index. The underlying equations of motion contain both holonomic constraints and servo constraints in which desired outputs (specified in time) are described in terms of state variables. The realization of servo constraints with the use of control forces can range from orthogonal to tangential [3]. Since the (differentiation) index of the DAEs is often higher than three for underactuated systems, in which the number of degrees of freedom is greater than the control outputs/inputs, we propose a new index reduction method [1] which makes possible the stable numerical integration of the DAEs. We apply the proposed method to differentially flat systems, such as cranes [1,4,5], and non-flat underactuated systems. (© 2016 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Stabilization of the motions of mechanical systems in prescribed phase-space manifolds

A method for constructing a mathematical model of the dynamics of a mechanical system is proposed. An algorithm is constructed for determining the expressions for the control forces and the components of the constraint reactions. A modification is made to the dynamic equations which enables one to solve the problem of stabilizing the constraints and which ensures the required accuracy in the numerical solution of the corresponding system of differential-algebraic equations describing the constraints imposed on the system, its kinematics and dynamics. By virtue of well-known dynamic analogies, the proposed method can be used to investigate the dynamics of different physical systems. The problem of modelling the dynamics of an adaptive optical system with two degrees of freedom is considered.