基于回声状态网络的BOD在线软测量模型

针对污水处理过程的BOD建模问题,提出了一种基于回声状态网络的BOD在线软测量方法;基于梯度下降规则对回声状态网络的在线学习算法进行了研究;为保证学习算法的收敛性,基于Lyapunov理论对学习率范围进行了确定;实验表明,基于回声状态网络的在线BOD预测方法较常规神经网络预测精度提高约两个数量级,模型的适应性也大幅提高。     

Accommodation and convergence in integral imaging 3D display

In this paper, we analyze the visual fatigue of integral imaging three‐dimensional (3D) display by studying the accommodation and convergence when viewing the integral imaging 3D images. The theoretical analyses show that there is no inconsistency between the accommodation and convergence distances, which means that the integral imaging 3D display has no visual fatigue. Experiments are implemented, and the results finally demonstrate the rationality of the theoretical analyses. These analyses can be quite beneficial for researchers to better understand the performances and promote the practical applications of the integral imaging 3D display.     

基于终态神经网络的冗余机械臂重复运动规划

为解决冗余机械臂在运动过程中出现的关节角漂移现象,提出了一种终态吸引优化指标,形成冗余机械臂重复运动规划的二次优化方法。采用具有有限值激活函数的终态神经网络来求解,在初值位置偏移目标位置的情形下,实现冗余机械臂有限时间收敛的重复运动规划任务。同时,分别以新型的终态神经网络(TNN)和其加速网络(ATNN)求解运动规划问题,该网络求解方法具有终态吸引特性,能够在有限的时间内得到有效解。相比具有渐近收敛动态特性的神经网络(ANN),终态神经网络方法不仅改变了收敛速度,而且提高了收敛的精度。基于冗余机械臂PUMA560的计算机仿真结果表明了所提方法的有效性和实时性。     

基于学习的核偏最小二乘法优化扩展卡尔曼滤波

为了解决非线性系统参数不确定和噪声信息不准确导致EKF随时序估计误差较大的问题,利用核偏最小二乘法与系统方程参数和噪声信息无关的特点优化EKF。先将量测数据和EKF的收敛估计作为学习样本,建立KPLS预测模型,然后,融合KPLS和EKF的预测值进行状态估计;同时,若状态估计的收敛判据为真,将估计值作为学习样本,并利用滑动窗口更新KPLS核矩阵,使KPLS能时序预测;若收敛判据为假,则更新量测协方差。最后,通过实验仿真的方法,分析KPLS-EKF算法的收敛性和性能。实验结果表明：KPLS-EKF能够有效地解决非线性系统参数和噪声信息不准确导致的EKF误差较大的问题。     

基于定向爬山的遗传算法

爬山法是一种局部搜索能力相当好的算法,主要是因为它是通过个体的优劣信息来引导搜索的。而传统的遗传算法作为一种全局搜索算法,在搜索过程中却没有考虑个体间的信息,而仅依靠个体适应度来引导搜索,使得算法的收敛性受到限制。将定向爬山机制应用于遗传算法,提出了一种基于定向爬山的遗传算法（OHCGA）。该算法结合了爬山法与遗传算法的优点,通过比较个体的优劣,使用定向爬山操作引导算法向更优秀的解区域进行搜索。实验结果表明,与传统遗传算法（TGA）相比,OHCGA较大地提高了算法的收敛速度和搜索最优解的能力。     

基于最速下降最优解参考的粒子群算法

将现代智能优化算法(微粒群算法)和最速下降法有机的结合起来,构造出一种混合优化算法,该算法既有微粒群算法的优点,又有最速下降的较高收敛性和精度,数值计算表明算法对于求解连续可微函数的全局优化问题是非常有效的。     

Convergence Analysis of Weighted Stochastic Gradient Identification Algorithms Based on Latest‐Estimation for ARX Models

In this paper, weighted stochastic gradient (WSG) algorithms for ARX models are proposed by modifying the standard stochastic gradient identification algorithms. In the proposed algorithms, the correction term is a weighting combination of the correction terms of the standard stochastic gradient (SG) algorithm in the current and last recursive steps. In addition, a latest estimation based WSG (LE‐WSG) algorithm is also established. The convergence performance of the proposed LE‐WSG algorithm is then analyzed. It is shown by a numerical example that both the WSG and LE‐WSG algorithms can possess faster convergence speed and higher convergence precision compared with the standard SG algorithms if the weighting factor is appropriately chosen.     

钢筋混凝土斜向受力压弯构件试验及全过程分析

本文通过18根钢筋混凝土斜向受力压弯构件的试验研究和98根模型构件的非线性全过程分析,探讨了水平荷载的作用方向、轴压比、配箍率及纵筋布置方式等对钢筋混凝土压弯构件抗震性能的影响,分析了斜向受力压弯构件的扭转变形。     

A smoothing inexact Newton method for variational inequality problems

In this paper, we reformulate the variational inequality problem as an equivalent smooth non-linear equation system by introducing the Chen–Harker–Kanzow–Smale smoothing function. A new smoothing inexact Newton algorithm is proposed to solve the smooth equations. In each iteration, the corresponding linear system is solved approximately. We prove that the proposed algorithm converges globally and superlinearly under mild conditions. Preliminary numerical results indicate that the method is effective.     

Modified alternating direction-implicit iteration method for linear systems from the incompressible Navier–Stokes equations

In order to solve the large sparse systems of linear equations arising from numerical solutions of two-dimensional steady incompressible viscous flow problems in primitive variable formulation, Ran and Yuan [On modified block SSOR iteration methods for linear systems from steady incompressible viscous flow problems, Appl. Math. Comput. 217 (2010), pp. 3050–3068] presented the block symmetric successive over-relaxation (BSSOR) and the modified BSSOR iteration methods based on the special structures of the coefficient matrices. In this study, we present the modified alternating direction-implicit (MADI) iteration method for solving the linear systems. Under suitable conditions, we establish convergence theorems for the MADI iteration method. In addition, the optimal parameter involved in the MADI iteration method is estimated in detail. Numerical experiments show that the MADI iteration method is a feasible and effective iterative solver.