H^∞距离问题中关于虚轴上一般变秩的研究

计算H~∞问题中α=inf{‖T_1-T_2QT_3‖_∞:Q∈RH_∞}的α和Q,要求T_2(jw)和T_3(jw)在jw轴上保持秩不变本文给出T_2(jw)和T_3(jw)在jw轴上秩任意变化时的结果。     

强正规剖分下有限元方法的最大模估计、外推与应力佳点

1 引言 二阶线性椭园型边值问题有限元逼近的L_∞估计,。已有很多结果。例如、、等,Fried 最近通过一个例子说明,在通常条件下,逐点估计, ||u-u~b||0∞≤Ch~2 1n1/h||u||2∞ (1)已是最优的了。不过,如果有某些附加条件,上述结果还可以在精度上得到改进。例如,当给定区域的三角剖分是强正规时(可看、或第2节)且u∈H~3(Ω)∩W_∞~2     

中立型泛微分方程的一类混合方法

其中α<α,g(t)∈C_n~0(I_t)为已知的初值函数,f:I×C_n~0(I)×C_n~1(I)→R~n,满足下列条件: H_1:对于固定的X∈C_n~1(I),映射t→f(t,x(·),t’(·))在I上连续。 H_2:算子f满足Lipschitz条件 ||f(t,x_1(·),y_1(·))-f(t,x_2(·),y_2(·))||≤L_1||x_1-x_2||~[α,t]+L_2||y_1-y_2||~[α,t-δ],其中L_1,L_2≥0为常数,δ>0,t∈I,x_1,x_2∈C_n~0(I),y_1,y_2∈C_n~1(I)。     

找到不可约多项式的研究与FoxPro程序实现

在扩频通信与传统的密码体制中广泛使用的伪随机序列,大部分是利用不可约多项式通过反馈位移寄存器和其他非线性逻辑来产生的。同时,多项式理论,特别是不可约多项式的性质对分析各种伪随机序列有着特殊的作用。 (1)找到非负整系数不可约多项式的原理 建立非负整系数多项式与正整数的符号对应和运算对应:设α_0,α_1,α_2,α_3,…,α_n是非负整数,2,3,5,7,…,p_n是n+1个相邻的素数。用正整数2~(α_0)3~(α_1)5~(α_2)(?)p_n~(α_n)表示非负整系数多项式α_0+α_1x+α_2x~2+…+α_nx~n。记作:{α_0+α_1x+α_2x~2+…+α_nx~n}←→2~(α_0)3~(α_1)5~(α_2)(?)p_n~(α_n)。     

热惯量成像研究

一、热惯量对经典的热传导方程给予一个简单的温度周期函数为边值条件,求解方程,可以得到一个简单的公式: Q=-2/ω~(1/2)T_0P (1)式中Q为热能,T_0为温度变化的振幅,ω为周日角频率,P是物体固有的常量,其表达式为: P=Kρc~(1/2) (2)其中K为热导率(焦耳/米·秒·开尔温),ρ为密度(千克/米~3),c为比热容(焦耳/(千克·开尔温)。从式(1)可见,当物体吸收或损失的热能相同时,物体温度变化的振幅T_0与物体固有的     

Ω上三重积分优化复化Simpson数值算法

设空间区域 Ω={(x,y,z)|α≤x≤b,φ_1(x)≤y≤φ_2(x),φ_1(x,y)≤z≤φ_2(x,y)}。(1)f(x,y,z)在Ω及其邻域内具有四阶连续偏导数,φ_1(x)与φ_2(x)在[α,a]内可导,φ_1(x,y)与φ_2(x,y)在Ω的投影(xoy面)区域上具有连续偏导数。下面介绍三重积分 I=∫∫∫f(x,y,z)dxdydz (2)的优化复化Simpson数值积分算法。首先将Ω进行划分,把[α,b]分为2m等分,步长与分点为 h_1=(b-α) /2m,x_i=α+ih_1(i=0,1,2,…,2m)。 (3)在x_(2i+1)处把[φ_1(x_(2i+1)),φ_2(x_(2i+1))分为2n等分,步长与分点为 g_(1,2i+1) =((φ_2(x_(2i+1)))-(φ_1(x_(2i+1))))/2n (i=o,1,2,…,m-1), (4) y_(2i+1,j)=φ_1(x_(2i+1))+jg_(1,2i+1) (j=0,1,2,…,2n)。     

合成序列变换的加速收敛

设{S_n}是待加速的序列,limS_n=S。按[1]考虑序列变换t_k:{S_n}→{t_k~(n),k=1,2。记 N_k={{S_n}:?N,n>N,t_k~(n)=S},称N_k(k=1,2)是变换t_k的核。定义变换T T:{S_n}→{T_n}, ?_n,T_n=(1-α_n)t_1~(n)+α_nt_2~(n),并规定,若S_n∈N_1,则?n,α_n=0,若S_n∈N_2,则?n,α_n=1。此时称T是秩为2的合成序列变换。 记N是变换T的核,则N?N_1∪N_2。由此说明变换T优于变换t_1和变换t_2。     

解无约束最优化问题的梯度加速法

一、问题的提出 关于无约束最优化问题 min f(x), x∈R~n在[1]中曾提到曲线线性搜索是一个求解这类问题的令人感兴趣的研究课题,采用的一般曲线为 x(α)=x+Φ_1(α)8+Φ_2(α)p(a≥0参数),其中Φ_1(0)=Φ_2(0)=0,且Φ_1,Φ_2应满足条件 Φ_1~′(0)=0,Φ_2~′(0)>0,Φ_1~″(0)>0.最简单的是取     

数字PID调节参数的一种最优整定法

在工业过程控制领域,PID调节器占有十分重要的位置,整定PID调节器参数的方法有多种。本文利用最优控制中线性二次性能指标来整定PID调节参数,从而获得最优PID调节。一、对象的数学模型在工业控制中,常会遇到下述对象: Ke~(-τs)/(ST 1)(1) Ke~(-τs)/S(ST 1)(2) Ke~(-τs)/(T_1S 1)(T_2S 1)(3) Ke~(-τs)(aS~2 bS C)(4) 显然式(1)～式(3)都可归纳成式(4)所示     

求泛函φ(P)=sup x∈X F(x,P(x))极小解的若干方法

设C(X)为紧集X上的连续函数空间,M C(X)为n维子空间.其中n为自然数, φ_1,…,φ_n为它的一个基底.对X上任意实值函数,定义||f||=sup x∈X|f(x)|.又设F(x,y) 为X×(-∞,∞)上的非负二元函数,且 e_0≡||F(x,0)||<∞ (1) 现提出如下的极小问题:对于闭集K M(今后为讨论方便起见常假定O∈K)寻找 一个P∈K使它满足     

动态交通分配与信号控制的组合模型及算法研究

This paper presents a generalized bi-level programming model of combined dynamic traffic assignment and traffic signal control, and especially analyzes a procedure for determining the equilibrium queuing delays on saturated links for dynamic network signal control satisfying the FIFO (first-in-first-out) rule. The chaotic optimal algorithm proposed in this paper can not only present the optimal signal settings, but also calculate, at each interval, the link inflow rates and outflow rates for the dynamic user optimal problem, and provide real-time information for the travelers. Finally, a numerical example is given to illustrate the application of the proposed model and solution algorithm, and comparison shows that this model has better system performance.     

Research on Combined Dynamic Traffic Assignment and Signal Control

This paper presents a generalized bi-level programming model of combined dynamic traffic assignment and traffic signal control,and especially analyzes a procedure for determining the equilibrium queuing delays on saturated links for dynamic network signal control satisfying the FIFO (first-in-first-out)rule.The chaotic optimal algorithm proposed in this paper can not only present the optimal signal settings,but also calculate,at each interval,the link inflow rates and outflow rates for the dynamic user optimal problem,and provide real-time information for the travelers.Finally,a numerical example is given to illustrate the application of the proposed model and solution algorithm, and comparison shows that this model has better system performance.     

面向空间目标交汇的多拦截器最优部署算法

对空间目标的交汇拦截是现代空天防御体系部署的关键环节, 而其核心优化控制问题为实现交汇次数的最大化. 本文针对一类多个拦截器与空间目标交汇的最优问题, 提出了同时优化拦截器初始部署位置和机动过程中加速度的最优控制策略. 首先, 建立了同时考虑了部署地域和动力学约束的交汇次数最大化的最优控制模型. 进一步, 给出了拦截器与目标可交汇的必要条件以及最优加速度输入设计, 进而使得将最优控制问题转为拦截器部署位置的优化问题. 最后, 严格给出了各个拦截器最优部署位置的具体计算过程.     

有限时间信息融合线性二次型最优控制

针对有限时间线性二次型最优控制问题, 提出了一种新的求解方法—–信息融合估计方法. 基于线性最小方差估计准则下的融合估计理论, 通过融合期望状态轨迹、理想控制策略等软约束信息, 分别采用集中式融合和序贯式融合两种信息处理方法, 求得最优状态调节器问题的最优融合控制序列. 进一步从理论上论证了序贯式融合控制方法与传统最优控制方法的一致性, 并通过直流电机系统的数值仿真也验证了集中式和序贯式融合控制方法 与传统最优控制方法的等效性, 从而统一了最优估计与最优控制问题, 并为最优控制问题提供了一种新的求解方法.     

基于LQR最优调节器的倒立摆控制系统

倒立摆控制系统是一个典型的高阶次、不稳定、多变量、非线性和强藕合控制系统。本文研究分析了单节倒立摆控制系统的数学模型，介绍了线性二次型最优调节器(LQR)的基本原理，并用该方法实现对单节倒立摆的最优控制，MATLAB仿真结果和实验结果表明了该方法的有效性。     

Optimal Nonlinear Income Taxation with a Two-Dimensional Population; A Computational Approach

We consider the optimal income tax problem when income differences are due to differences in abilities and in preferences between consumption and leisure among individuals. We model this problem as an optimal control problem and develop a numerical method for solving it. The method is based on the expansion of state and control variables in Lagrange series and on a spectral collocation method for approximating state equations. In this way the optimal control problem is reduced to a parameter optimization problem. The problem is difficult to solve, but we managed to do so with some limitations. On the basis of our calculations we conclude that the tax system in the two-dimensional case is more redistributive compared to that obtained from the one-dimensional model.     

关于最佳鉴别特征维数问题的讨论

该文对最佳鉴别特征的最佳维数问题进行了详细的讨论．文章首先对最佳维数问题进行了界定，然后指出了两种最佳特征维数为c-1维的情况即以某些基于矩的可分性判据(准则函数)为优化目标的最优特征和以某些特殊的分类器错误率为优化目标的最优特征．最后该文运用方差分析法对最佳鉴别特征进行特征选择使之代入最小距离分类器后识别率最大．     

ROBUST STABILIZATION IN THE BIBO GAP TOPOLOGY

We consider robust stabilization of both linear causal discrete-time systems in an l¹-setting and linear causal continuous-time systems in an L¹-setting. We introduce a new metric in the space of l¹(L¹)-stabilizable systems in terms of their coprime factorizations. This metric is easily computed and induces the gap topology. We show how robustness optimization in this metric is related to robustness optimization for normalized factor perturbations. In each case, the optimal controller determined by Glover and McFarlane (and studied byGeorgiou and Smith) in the l² (L²)-setting plays an important role. Finally we show that this metric is easily linked to approximation and identification. © 1997 by John Wiley & Sons, Ltd. Int. J. Robust Nonlinear Control, Vol. 7, 429–447 (1997)     

移动机器人速度加速度饱和约束下的时间最优控制

针对机器人运动系统中普遍存在的速度和加速度约束, 提出一种满足以上约束的机器人运动时间最优控制方法. 首先, 通过最优条件构造哈密尔顿函数, 根据极小值原理求解时间最优控制; 其次, 通过相轨迹分析, 证明了满足约束的时间最优控制律的形式; 再次, 通过求解最优时间, 将满足约束的时间最优控制律转换成末端时间为最优时间的燃料最优控制律; 最后, 在RoboCup 小型足球机器人上进行对比实验, 验证了该方法在规划与实际上的一致性.     

Time/fuel optimal control of constrained linear discrete systems

A unified approach to solving three common optimal control problems is presented, for linear systems under general constraints. The problems are: (1) the time optimal control problem; (2) the fuel optimal control problem in fixed time; (3) the time optimal control problem with a fuel constraint. A special purpose linear programming algorithm is used. State variable constraints are efficiently handled by a cutting plane algorithm. An example of a sixth order system with two inputs and two state variable constraints illustrates the method as implemented on a personal computer.