求解广义支持向量机的光滑型算法

提出一种求解支持向量机(SVMs)的光滑型算法.该算法基于其对偶优化模型的KKT系统,提出一类新的光滑函数族,将其KKT系统重构为一个光滑方程组,并采用光滑型算法进行求解.在适当的条件下,该算法是全局收敛和局部超线性收敛的.多个算例表明该算法非常有效,具有广阔的应用前景.     

应用非单调线搜索求解一类互补问题

考虑一类含非Lipschtizian连续函数的非线性互补问题。引入plus函数的一类广义光滑函数,讨论其性质。应用所引入函数将互补问题重构为一系列光滑方程组,提出一个具有非单调线搜索的Newton算法求解重构的方程组以得到原问题的解。在很弱的条件下,该算法具有全局收敛性和局部二次收敛性。利用该算法求解一自由边界问题,其数值结果显示该算法是有效的。     

非光滑非线性系统的非光滑连续控制

针对具有非光滑非线性的系统,提出了一种非光滑连续控制方法,通过非光滑建模方法能够快速精确地补偿系统中有害的非光滑非线性,同时通过非光滑连续控制引入一些有益的非光滑非线性以获得快速高精度的控制性能,给出了实验迟滞曲线和建模结果。讨论了该项技术在非光滑非线性控制系统中的应用前景。     

含间隙齿轮传动系统的非线性动力学特性的研究

含间隙齿轮传动系统存在着丰富的非线性动力学行为,建立了两自由度齿轮传动系统的动力学模型,首先利用数值方法分析了齿轮传动系统的分岔和混沌动力学行为,然后将非光滑的间隙函数用光滑函数拟合,通过对系统进行多尺度摄动分析和计算,得到间隙函数拟合后的齿轮传动系统的非线性动力学现象,并与无拟合非光滑系统的数值结果进行比较分析,为含间隙齿轮传动系统的研究奠定了基础.     

基于极大熵牛顿法求解绝对值方程

针对绝对值方程Ax-|x|=b的求解问题,在假设矩阵A的奇异值大于1时,给出了求解绝对值方程的一个新方法。通过引入一种极大熵函数将绝对值方程进行光滑化处理,进而把绝对值方程转换为光滑非线性方程组,然后利用极大熵牛顿法对其进行求解。数值实验结果表明了该方法的正确性和有效性。     

一类非线性控制系统关于非光滑区域生存性的判别

讨论微分包含关于非光滑区域生存性的判别问题．当右端集值映射为多面体，边界为次可微函数，且次微分为有限点集凸包时，基于非光滑分析理论，给出了在一点处检验生存性条件是否成立的方法．该方法将生存性判别转化为判别线性不等式组的相客性或等价地转化为求解一个线性规划问题．最后讨论了次可微函数上图的生存性问题。     

基于光滑l0范数和修正牛顿法的压缩感知重建算法

基于光滑l0范数最小的压缩感知重建算法——SL0算法,通过引入光滑函数序列去逼近l0范数,从而将l0范数最小的问题转化为光滑函数的最优化问题.针对光滑函数的选取以及求解该函数的最优化问题,提出一种基于光滑l0范数和修正牛顿法的重建算法——NSL0算法.首先采用双曲正切函数序列来逼近l0范数,得到一个新的最优化问题;为了提高该优化问题的计算效率,推导出针对双曲正切函数的修正牛顿方向,并采用修正牛顿法进行求解.实验结果表明,在相同的测试条件下,NSL0算法无论在重建效果还是在计算时间方面都明显优于其他同类算法.     

求非光滑规划全局极小点的一类改进的填充函数法

本文考虑优化问题limF(x),其中F(x)为非光滑函数,引入了求解该优化问题的一类改进的双参数填充函数,给出了相应的算法及收敛域估计,理论分析及数值结果均表明该方法是行之有效的.     

光滑神经网络解决非李普西茨优化问题的研究

为寻求满足约束条件的优化问题的最优解,针对目标函数是非李普西茨函数,可行域由线性不等式或非线性不等式约束函数组成的区域的优化问题,构造了一种光滑神经网络模型。此模型通过引进光滑逼近技术将目标函数由非光滑函数转换成相应的光滑函数以及结合惩罚函数方法所构造而成。通过详细的理论分析证明了不论初始点在可行域内还是在可行域外,光滑神经网络的解都具有一致有界性和全局性,以及光滑神经网络的任意聚点都是原始优化问题的稳定点等结论。最后通过几个简单的仿真实验证明了理论的正确性。     

基于非光滑观测器的带死区三明治系统状态估计

在控制工程实际中,许多含有死区的系统可以用带死区的三明治系统描述．本文针对带死区的三明治系统特点,构建了一种非光滑观测器以对系统状态进行估计．首先根据带死区三明治系统的特点,由分离原理,建立了描述系统特性的非光滑状态空间方程．据此构造了能够随系统工作区间变化而自动切换的非光滑观测器,给出了相应的收敛定理及其证明．最后通过仿真,分别比较了非光滑观测器和传统的观测器对状态的跟踪效果,比较结果表明非光滑观测器对于带死区三明治系统状态变量估计的准确性要优于传统的观测器．     

A nonsmooth Newton method for elastoplastic problems

In this work we reformulate the incremental, small strain, J₂-plasticity problem with linear kinematic and nonlinear isotropic hardening as a set of unconstrained, nonsmooth equations. The reformulation is done using the minimum function. The system of equations obtained is piecewise smooth which enables Pang's Newton method for B-differentiable equations to be used. The method proposed in this work is compared with the familiar radial return method. It is shown, for linear kinematic and isotropic hardening, that this method represents a piecewise smooth mapping as well. Thus, nonsmooth Newton methods with proven global convergence properties are applicable. In addition, local quadratic convergence (even to nondifferentiable points) of the standard implementation of the radial return method is established. Numerical tests indicate that our method is as efficient as the radial return method, albeit more sensitive to parameter changes.     

求解最优潮流KKT系统的一类新模型及算法设计

电力工业的市场化改革对最优潮流(optim al pow er flow,OPF)的计算精度和速度提出了更高的要求.本文针对OPF模型中存在大量的无功界约束的特性,把一般非线性不等式约束和界约束分开处理,通过引入一个对角矩阵和非线性互补函数,建立了与OPF问题的K arush-Kuhn-Tucker(KKT)系统等价的约束非光滑方程新模型.进一步,基于新建立的模型,提出了一类具有理论上收敛性保证的投影半光滑N ew ton型算法.相对于传统的解OPF的KKT系统和非线性互补函数方法,新方法一方面保持了非线性互补函数法无需识别有效集的优点,同时又减少了问题的维数,且投影计算保持了无功界约束的可行性.IEEE多个算例的数值试验显示本文所提出的模型和算法具有较好的计算效果.     

A verification method for solutions of nonsmooth equations

This paper proposes a verification method for the existence of solutions of nonsmooth equations. We generalize the Krawczyk operator to nonsmooth equations by using the mean-value theorem for nonsmooth functions. We establish a semi-local convergence theorem for the generalized Newton method for nonsmooth equations. The proposed method is a combination of the generalized Krawczyk operator and the semi-local convergence theorem.     

Feedback stabilization for a class of discontinuous systems driven by integrator

This paper investigates the feedback stabilization problem for a class of discontinuous systems which is characterized by Filippov differential inclusion. Lyapunov-based backstepping design method is generalized with nonsmooth Lyapunov functions to solve the control problem. A set-valued time derivative is introduced first for nonsmooth function along discontinuous vector fields, which enables us to perform Lyapunov-based design with nondifferentiable Lyapunov function. Conditions for designing a virtual control law which is shown nondifferentiable in general in the recursive design problem are proposed. Finally, as a special case, piecewise linear system is discussed to demonstrate the application of the presented design approach.     

A Nonsmooth Nonlinear Programming Based Predictive Control for Mechanical Servo Systems with Backlash‐like Hysteresis

In this paper, a multi‐step‐ahead predictive control approach for dynamic systems with preceded backlash‐like hysteresis based on nonsmooth nonlinear programming is proposed. In this approach, a nonsmooth multi‐step‐ahead predictive model is developed for long‐range prediction of the controlled dynamic systems with preceded backlash‐like hysteresis. Then, the predictive control strategy is treated as a problem of on‐line nonsmooth nonlinear programming. Subsequently, the stability of the nonsmooth predictive control system is analyzed and the corresponding stability condition is derived. Afterward, a numerical example and a simulation based on a mechanical servo system are presented, respectively.     

A nonsmooth Newton’s method for control-state constrained optimal control problems

We investigate optimal control problems subject to mixed control-state constraints. The necessary conditions are stated in terms of a local minimum principle. By use of the Fischer–Burmeister function the minimum principle is transformed into an equivalent nonlinear and nonsmooth equation in appropriate Banach spaces. This nonlinear and nonsmooth equation is solved by a nonsmooth Newton’s method. We will show the local quadratic convergence under certain regularity conditions and suggest a globalization strategy based on the minimization of the squared residual norm. A numerical example for the Rayleigh problem concludes the article.     

求解互补问题的极大熵社会认知算法

针对传统算法无法获得互补问题的多个最优解的困难,提出了求解互补问题的社会认知优化算法.通过利用NCP函数,将互补问题的求解转化为一个非光滑方程组问题,然后用凝聚函数对其进行光滑化,进而把互补问题的求解转化为无约束优化问题,利用社会认知算法对其进行求解.该算法是基于社会认知理论,通过一系列的学习代理来模拟人类的社会性以及智能性从而完成对目标的优化.该算法对目标函数的解析性质没有要求且容易实现,数值实验结果表明了该方法是有效的.     

Some implementation issues associated with multidimensional interval Newton methods

This paper presents the development of an optimal interval Newton method for systems of nonlinear equations. The context of solving such systems of equations is that of optimization of nonlinear mathematical programs. The modifications of the interval Newton method presented in this paper provide computationally effective enhancements to the general interval Newton method. The paper demonstrates the need to compute an optimal step length in the interval Newton method in order to guarantee the generation of a sequence of improving solutions. This method is referred to as the optimal Newton method and is implemented in multiple dimensions. Secondly, the paper demonstrates the use of the optimal interval Newton method as a feasible direction method to deal with non-negativity constraints. Also, included in this implementation is the use of a matrix decomposition technique to reduce the computational effort required to compute the Hessian inverse in the interval Newton method. The methods are demonstrated on several problems. Included in these problems are mathematical programs with perturbations in the problem coefficients. The numerical results clearly demonstrate the effectiveness and efficiency of these approaches.     

On the iterated forms of Kalman filters using statistical linearization

The extended Kalman filter (EKF) is a suboptimal estimator of the conditional mean and covariance for nonlinear state estimation. It is based on first order Taylor series approximation of nonlinear state functions. The unscented Kalman filter (UKF) and the ensemble Kalman filter (EnKF) are suboptimal estimators that are termed as Jacobian free because they do not require the existence of the Jacobian of the nonlinearity. The iterated form of EKF is an estimator of the conditional mode that employs an approximate Newton–Raphson iterative scheme to solve the maximization of the conditional probability density function. In this paper, the iterated forms of UKF and EnKF are presented that perform Newton–Raphson iteration without explicitly differentiating the nonlinear functions. The use of statistical linearization in iterated UKF and EnKF is a nondifferentiable optimization method when the measurement function is nonsmooth or discontinuous. All three iterated forms can be shown to be conditional mean estimators after the first iteration. A simple numerical example involving continuous and discontinuous measurment functions is included to evaluate the performance of the algorithms for the estimation of conditional mean, covariance and mode. A batch reactor simulation is shown for estimating both the states and unknown parameters.     

Equivalence of nonlinear systems to triangular form: the singular case

The problem of state equivalence of a given nonlinear system to a triangular form is considered here. The solution of this problem has been known for the regular case, i.e. when there exists a certain nested sequence of regular and involutive distributions. It is also known that in this case the corresponding system is linearizable using a smooth coordinate change and static state feedback. This paper deals with the singular case, i.e. when the nested sequence of involutive distributions of the system contains singular distributions. Special attention is paid to the so-called bijective triangular form. Geometric, coordinates-free criteria for the solution of the above problem as well as constructive, verifiable procedures are given. These results are used to obtain a result in the nonsmooth stabilization problem.