基于BP算法的地下水模拟中 加速因子的确定

在大型线性方程组的超松弛迭代法求解中,加速因子经常难以确定.应用BP神经网络对其进行训练学习,经过对比分析,得到最佳模型,应用该模型可快速确定加速因子.将该方法应用于石家庄市栾城水文试验基地,计算结果表明,BP人工神经网络有效地解决了地下水数值模拟中加速因子难以确定的问题.     

多站测向交叉定位中的非线性改进最小二乘法

多站测向定位技术是使用最广泛的无源定位技术,通常使用最小二乘方法对目标位置进行估计定位。目前计算定位估计的最小二乘法主要是线性近似法或者牛顿迭代法,然而线性近似法存在定位不精确、无法满足定位系统高度的非线性等缺陷,牛顿法存在定位结果不稳定、对初值敏感、Hessian矩阵奇异无法使用等不足,因此定位精度不高。本文以线性近似得到的结果作为牛顿迭代的初值,从而降低牛顿迭代法发散的几率,增加其稳定性,提高了算法效率。首次在使用牛顿迭代法进行定位估计中解决了Hessian矩阵奇异无法计算的情况,极大地提高的牛顿迭代法的适用性,为目标定位系统提供了更好的定位方法,具有很高的应用价值和很好的应用前景。     

企业集团供应链风险的识别与评价研究

集团化发展使企业供应链网络结构日趋复杂,供应链的脆弱和不稳定性带来了许多风险.如何识别和评估风险成为供应链管理的核心问题.文章对供应链风险进行了系统识别和分类,分析了供应链风险的可控性和危害性.构建了供应链风险评估指标体系,利用改进的集值统计加速迭代法确定各指标权重,建立了基于专家风险概率区间估计的供应链风险综合评价模型,并进行了实证分析.     

结构非线性分析求解器的设计与实现

本文介绍了一个非线性有限元方程求解器的设计与实现。弧长法、荷载增量法、修正牛顿-拉夫逊迭代法、完全牛顿-拉夫逊迭代法、线性搜索技术和在拟牛顿法基础上建立起的加速求解技术等是成熟的非线性有限元方程求解技术。本文将这些方法有机结合,建立了一个非线性方程求解器。数值算例证明这是一个有效、可靠的求解器。     

叶肉结构对叶片光谱及生化组分定量反演的影响研究

深入研究了PROSPECT模型中叶肉结构参数N对叶片反射和透射光谱的影响，利用迭代法使全波段代价函数达到最小来计算LOPEX93数据集中样品的最优N值，对在最优N值下的模拟光谱与实测光谱进行比较，并对N在不同取值情况下，叶绿素与水分的反演进行了研究。研究认为：（1）N对整个光谱波段产生影响，并随N的增大而减小；（2）构造全波段代价函数，利用迭代法得到的最优N值可以很好地模拟实际光谱；（3）在高估N值情况下的叶绿素和水分反演精度明显高于低估N值的情况；（4）水分反演的效果明显优于叶绿素反演的效果，原因与代价函数的选取有关。     

求解非线性方程的数值算法

本文通过对于求解非线性方程问题的五种方法即二分法、牛顿法、简易牛顿法、割线法、steffensen法等数值分析方法的算法原理及实现方法的研究,通过对非线性方程开普勒方程x-asinx=b(分别对a,b赋值1,2)运用以上五种不同的迭代法进行求解,对几种迭代法的运算量、迭代步数、所用时间进行分析与评价,最终对其进行比较,得到五种不同迭代方法的适于求解的非线性方程类型的简要分析推断。     

基于多元线性回归的煤质特征分析及预测模型

为了研究分析煤质特征,本文对全国多个地区具有代表性的10种变质程度的煤样进行了工业分析和元素分析实验,介绍各煤质参数在煤中的作用及获得方法。研究了煤质参数随煤化程度的变化关系。基于Matlab软件对煤质间参数进行多元线性回归分析,建立煤质参数之间的数学预测模型,用最小二乘法估计未知参数,提出"循环因子迭代法"加速拟合。得出Odaf%的回归效果最好,相关系数R2达到0.9976,P=0.00080.01。由此对研究煤的其他性质分析奠定了基础。     

求解非线性方程的牛顿迭代法

本文主要讲了求解非线性方程的牛顿迭代法。文章首先引入牛顿迭代法的公式、迭代函数。紧接着文章又介绍了牛顿迭代法的局部收敛性以及它的收敛速度,并通过数值实验验证了牛顿迭代法求解非线性方程的有效性。     

基于SQI模型的企业加速成长测度研究——以创业板上市企业为例

首次着眼于企业的加速成长性,从成长加速度的角度深入研究企业的成长潜力,构造了包含企业规模(Scale)、企业质量(Quality)以及企业创新能力(Innovation)三个维度的企业加速成长测度指标模型体系,简称企业加速成长的SQI模型,基于该模型,以加速度作为测度工具,对2010~2014年间创业板上市的71家企业进行了加速成长性测度研究,得到了这71家企业的加速成长性排名,结果显示一半以上企业实现了加速,具有加速成长性。最后,通过对创业板企业加速成长性的分析,初步探索出创业板上市公司的加速成长性趋势以及主要影响因素,并提出了相应的发展建议。     

抗差估计在拟合圆中的应用

针对目前测量实践中,拟合圆中观测坐标往往会存在粗差,导致参数精度降低这一具体问题,提出用抗差理论求解拟合圆参数。为此,本文讨论了传统的间接平差和选取迭代法如何实现拟合圆参数求解,并结合生产实践算例证明了选权迭代法能更可靠地得到拟合圆的参数。建议广大测绘工作者根据需要运用抗差理论估计拟合圆参数。     

Globally exponentially stable single beacon underwater navigation with unknown sound velocity estimation

Single beacon navigation methods with unknown effective sound velocity (ESV) have recently been proposed to solve the performance degeneration induced by ESV setting error. In these methods, a local linearization-based state estimator, which only exhibits local convergence, is adopted to estimate the navigation state. When the initial ESV setting error or vehicle initial position error is large, the local linearization-based state estimators have difficulty guaranteeing the filtering convergence. With this background, this paper proposes a linear time-varying single beacon navigation model with an unknown ESV that can realize global convergence under the condition of system observability. A Kalman filter is adopted to estimate the model state, and the corresponding stochastic model is inferred for the application of the Kalman filter. Numerical simulation confirms that the proposed linear time-varying single beacon navigation model can realize fast convergence in the case of a large initial error, and has superior steady-state performance compared with the existing methods.     

General value iteration based single network approach for constrained optimal controller design of partially-unknown continuous-time nonlinear systems

In this paper, a novel iterative approximate dynamic programming scheme is proposed by introducing the learning mechanism of value iteration (VI) to solve the constrained optimal control problem for CT affine nonlinear systems with utilizing only one neural network. The idea is to show the feasibility of introducing the VI learning mechanism to solve for the constrained optimal control problem from a theoretical point of view, and thus the initial admissible control can be avoided compared with most existing works based on policy iteration (PI). Meanwhile, the initial condition of the proposed VI based method can be more general than the traditional VI method which requires the initial value function to be a zero function. A general analytical method is proposed to demonstrate the convergence property. To simplify the architecture, only one critic neural network is adopted to approximate the iterative value function while implementing the proposed method. At last, two simulation examples are proposed to validate the theoretical results.     

Improved decentralized prescribed performance control for non-affine large-scale systems with uncertain actuator nonlinearity

This work considers a decentralized control problem for non-affine large-scale systems with non-affine functions possibly being discontinuous. A semi-bounded condition for non-affine functions is presented to guarantee the controllability, and the non-affine system is transformed to an equivalent pseudo-affine one based on the mild condition. Different from conventional control schemes on specific actuator nonlinearity, the controller proposed in this paper can deal with a series of actuator nonlinearities such as backlash and deadzone nonlinearity. A time-varying stable manifold involving the tracking error and its high-order derivatives is utilized to handle the high-order dynamics of each subsystem. Besides an improved prescribed performance controller independent of the initial condition is constructed to ensure the finite-time convergence of the error manifold to a predefined region. The boundedness and convergence of the closed-loop system are proved by Lyapunov theory and the counter-evidence method. Two examples are performed to verify the theoretical findings.     

Distributed fault detection for nonlinear multi-agent systems under fixed-time observer

This paper mainly investigates the fault detection problem for nonlinear multi-agent systems with actuator faults. For fault detection, a fixed-time observer is proposed by employing auxiliary variable received from neighbor agents. Then, with the aid of the observer, a residual vector is introduced by the auxiliary variable to detect the faults occurring on any followers, and each observer can estimate the whole state of followers. Moreover, the convergence time is dependent on the parameters of the designed observer and independent of initial condition of system state. Finally, the theoretical result is verified by a simulation example.     

The relaxed gradient-based iterative algorithms for a class of generalized coupled Sylvester-conjugate matrix equations

In this paper, two relaxed gradient-based iterative algorithms for solving a class of generalized coupled Sylvester-conjugate matrix equations are proposed. The proposed algorithm is different from the gradient-based iterative algorithm and the modified gradient-based iterative algorithm that are recently available in the literature. With the real representation of a complex matrix as a tool, the sufficient and necessary condition for the convergence factor is determined to guarantee that the iterative solution given by the proposed algorithms converge to the exact solution for any initial matrices. Moreover, some sufficient convergence conditions for the suggested algorithms are presented. Finally, numerical example is provided to illustrate the effectiveness of the proposed algorithms and testify the conclusions suggested in this paper.     

Adaptive learning tracking for robot manipulators with varying trial lengths

This paper proposes adaptive iterative learning control schemes for robot manipulator systems with iteration-varying lengths. To prove the asymptotical convergence of the joint position tracking error along the iteration axis, this paper develops a new composite energy function based on the newly introduced auxiliary variables for the analysis. Moreover, the traditional assumption of identical initialization condition is relaxed to be arbitrarily varying and then an initial rectifying mechanism is introduced to tackle initial shift problem of robotic systems. Illustrative simulations on a two degree-of-freedom robot manipulator are provided to verify the theoretical results.     

Predefined-time parameter estimation via modified dynamic Regressor extension and mixing

This paper presents a novel switching predefined-time parameter identification algorithm with a relaxed excitation condition based on the dynamic regressor extension and mixing (DREM) method. DREM often requires the persistent excitation (PE) of the extended square regressor's determinant to ensure exponential parameter convergence. Unlike the classical DREM method, a new parameter identification algorithm configured with a two-layer filter technique is proposed under a relaxed initial excitation (IE) condition, rather than strict PE. A key point in choosing IE instead of PE is the introduction of a smooth switching function that dominates the pure integral action and filter behavior of the extended square regressor. The proposed algorithm relies on the predefined-time stability theorem and the settling-time of the identification algorithm is set a priori as a system parameter. The contributions of this paper are a novel switching predefined-time parameter estimation algorithm that 1) relaxes the stringent PE condition, 2) achieves predefined-time convergence, and 3) guarantees the monotonicity of each element of the parameter error inherited from the classical DREM method. Comparative simulation results are presented to illustrate the effectiveness of the proposed algorithm.     

一个求多项式零点的并行迭代及其收敛的初始条件

首先构造了一个求多项式零点的并行迭代,然后对它的收敛性进行了分析,得出其收敛的两种类型的初始条件     

Varying-order iterative learning control against perturbed initial conditions

A homing mechanism is required for repositioning as a system performs tasks repeatedly. By examining the effect of poor repositioning on the tracking performance of iterative learning control, this paper develops a varying-order learning approach for the performance improvement. Through varying-order learning, the resultant system output trajectory is ensured to follow a given trajectory with a lowered error bound, in comparison with the conventional fixed-order method. A discrete-time initial rectifying action is introduced in the formed varying-order learning algorithm, and a sufficient condition for convergence is derived. An implementable scheme is presented based on the proposed approach, and illustrated by numerical results of two examples of robotic manipulators.     

Privacy-preserving weighted average consensus and optimal attacking strategy for multi-agent networks

This paper proposes a privacy-preserving consensus algorithm which enables all the agents in the directed network to eventually reach the weighted average of initial states, and while preserving the privacy of the initial state of each agent. A novel privacy-preserving scheme is proposed in our consensus algorithm where initial states are hidden in random values. We also develop detailed analysis based on our algorithm, including its convergence property and the topology condition of privacy leakages for each agent. It can be observed that final consensus point is independent of their initial values that can be arbitrary random values. Besides, when an eavesdropper exists and can intercept the data transmitted on the edges, we introduce an index to measure the privacy leakage degree of agents, and then analyze the degree of privacy leakage for each agent. Similarly, the degree for network privacy leakage is derived. Subsequently, we establish an optimization problem to find the optimal attacking strategy, and present a heuristic optimization algorithm based on the Sequential Least Squares Programming (SLSQP) to solve the proposed optimization problem. Finally, numerical experiments are designed to demonstrate the effectiveness of our algorithm.