数学竞赛中最值问题的构造法

最值问题在各级各类数学竞赛中经常出现 ,有些最值问题用常规方法处理有一定的难度 ,而采用构造法 s既巧妙、又简捷 ,能启发人的思维。本文通过实例浅谈一下具体应用。1　构造方程例 1 ,设两个实数 XY的平方和为 7,立方和为1 0 ,求 x+y的最大值。 (1 983年美国数学竞赛题 )解 :依题意 :x2 +y2 =7x3+y3=1 0令 :x+y=s,xy=t,即可构造如下方程s3- 2 1 s+2 0 =0　即 (s- 1 ) (s- 4) (s+5) =0因此　maxs=max(x+y) =4。2　构造图形例 2 ,求函数 f(x) =x4 - 5x2 +4x+1 3+x4 - 9x2 - 6x+34的最小值。解 :先将 f(x)变形为 :f(x) =(x- 2 ) 2 +(x2 - 3)…     

2形如 y=axbxc dx 2值域的求法探析exf

函数最值求法非常多,本文着重讨论形如 y= ax+bx+c dx 2(x∈D,a,d,不同时为0)的函数值域的求法,重点介绍双钩函数,换+ex+f元法,分离常数等方法在此类题型中的应用.     

带有低阶项的非散度椭圆方程解的二阶导数的高阶可积性

研究如下形式的非散度椭圆方程Lu=n∑i,j=1aij(x)(ε)2u/(ε)xi(ε)xj+n∑i=1bj(x)(ε)u/(ε)xi+c(x)u=h(x)解的二阶导数的高阶可积性,其中系数aij(x)有界且具有小BMO范数,bi(x),c(x)∈Ln(Ω),Ω为Rn(n≥3)中的有界光滑域.     

黎卡提方程的可积条件探析

给出了 Riccati 方程dy/dx:P(x)y2+Q(x)y+R(x)在系数P(x),Q(x),R(x).满足一定条件时的可积性.     

三角形面积公式的行列式形式及应用

从小学到中学,我们学习过好多个三角形面积公式,其中最主要的是和,但是当已知条件是三角形三个顶点的坐标时,使用上述两个公式就不很方便了,那么有没有更为简单的方法呢?笔者翻阅了许多参考书,找到了下面这个用行列式表示的三角形面积公式,但是始终没有找到这个公式的证明。鉴此,本文将应用向量的数量积公式和两直线的夹角公式等知识对该公式进行证明,并给出两个推论和应用实例。定理:在平面直角坐标系中,已知三角形三个顶点的坐标是A(x1,y1)、B(x2,y2)、C(x3,y3),则三角形ABC的面积公式是S=的绝对值。证明:由于三角形必有内角为锐角,不…     

费马猜想之证明

定理:p>2XP YP=ZP(1)中,p为奇素数,X,Y,Z无正整数解。证法之一:假设X,Y,Z均有正整数解。令X=x,Z=x a(a为正整数),Y=y0 a(y0为正整数),约定(x,y0,a)=1,则有:xp (y0 a)p=(x a)p(2)即:y0p c1pay0p-1 cp2a2yp0-2 …… cpp-1ap-1y0-cp1axp-1-c2pa2xp-2-……-cpp-1ap-1x=0(3)观察(3)式p｜y0,但由二项式定理二项式展开式通项公式得知:(y0 !a)p中,p!y0这是相互矛盾的,除非假设得到证明,(2)式这个等式成立,才等于明确指定(y0 a)p中y0含因子p,p｜y0才成立,在假设成为定理之前,矛盾始终存在。同样矛盾还有a｜yp0与a!y0p。当a｜y0p时,a必须为p次方…     

形如y=(ax~2+bx+c)/(dx~2+ex+f)值域的求法探析

函数最值求法非常多,本文着重讨论形如y=(ax～2+bx+c)/(dx～2+ex+f)(x∈D,a,d,不同时为0)的函数值域的求法,重点介绍双钩函数,换元法,分离常数等方法在此类题型中的应用。     

几类不定方程的解法

不定方程是数论中的一个古老分支 ,内容极其丰富。不定方程可以培养中学生、大学低年级学生的思维能力 ,因此不定方程经常出现在各类数学竞赛中。笔者建议在中学业余课堂、工科、财经类大学低年级适量开设这方面的课程 ,对于提高学生的素质、启迪思维是很有益处的。不定方程是指未知数的个数多于方程的个数的方程式方程组。本文通过实例给出几种方程的解法。1　 1x+ 1y+ 1z=a(a∈ N)型例 1,假设 x、y、z是三个不同的自然数 ,按上升次序排序 ,且它们的倒数之和仍然是整数 ,求 x、y、z。 (1918年匈牙利数学奥林匹克竞赛题 )解 :设 1x+ 1y+ 1z…     

导数在函数问题中的应用

导数内容的增加,为研究有关函数的问题开辟了一条新途径。利用导数求函数的单调区间,极大(小)值,利用函数解决一些实际应用题等成为高考命题的一个新热点。本文从以下几个方面来举例说明导数在函数问题中的应用。一、求函数的解析式例1设函数y=ax3+bx2+cx+d的图像与y轴交点为P点,且曲线在P点处的切线方程为12x-y-4=0,若函数在x=2处取得极值0,试确定函数的解析式。解:∵y=ax3+bx2+cx+d的图像与y轴的交点为P,∴P的坐标为(0,d),又曲线在P处的切线方程为y=12x-4,P点坐标适合方程,从而d=-4,又切线斜率k=12,故在x=0处的导数y'｜x=0=12,而y'=3ax…     

利用线性回归法研究足迹掌宽与身高的关系

目的:探明足迹掌宽与留痕人身高的关系。方法:采集72名自愿者(男53,女19)的正常赤足足迹样本和身高,应用一元线性回归的统计学方法,对赤足足迹的掌宽和留痕人身高进行统计分析。结果:留痕人身高与赤足足迹掌宽成正相关,存在线性关系,即左足为y=9.47x+94.17,右足为y=9.01x+97.88。结论:利用赤足足迹掌宽可以分析留痕人的人身特点。     

Iterative learning control for boundary tracking of uncertain nonlinear wave equations

This work presents a framework of iterative learning control (ILC) design for a class of nonlinear wave equations. The main contribution lies in that it is the first time to extend the idea of well-established ILC for lumped parameter systems to boundary tracking control of nonlinear hyperbolic distributed parameter systems (DPSs). By fully utilizing the system repetitiveness, the proposed control algorithm is capable of dealing with time-space-varying and even state-dependent uncertainties. The convergence and robustness of the proposed ILC scheme are analyzed rigorously via the contraction mapping methodology and differential/integral constraints without any system dynamics simplification or discretization. In the end, two examples are provided to show the efficacy of the proposed control scheme.     

数字水印提取的迭代学习辨识方法

针对结合混沌映射和多重混合的数字图像水印嵌入及提取问题,提出利用迭代学习辨识算法提取水印图像的方案。该方案将水印掩盖于混沌系统的参数中,以迭代学习算法重建原始水印图像。实验表明,运用所提出的迭代学习辨识算法能够从含水印图像中提取出完整的水印图像,给出的测试结果验证了学习算法抵抗常见几何攻击的能力。     

Adaptive iterative learning protocol design for nonlinear multi-agent systems with unknown control direction

This paper investigates a new adaptive iterative learning control protocol design for uncertain nonlinear multi-agent systems with unknown gain signs. Based on Nussbaum gain, adaptive iterative learning control protocols are designed for each follower agent and the adaptive laws depend on the information available from the agents in the neighbourhood. The proper protocols guarantee each follower agent track the leader perfectly on the finite time interval and the Nussbaum-type item can seek control direction adaptively. Furthermore, the formation problem is studied as an extension. Finally, simulation examples are given to demonstrate the effectiveness of the proposed method in this article.     

Consensus control via iterative learning for distributed parameter models multi-agent systems with time-delay

Most of the available results of iterative learning control (ILC) are that solve the consensus problem of lumped parameter models multi-agent systems. This paper considers the consensus control problem of distributed parameter models multi-agent systems with time-delay. By using the knowledge between neighboring agents, considering time-delay problem in the multi-agent systems, a distributed P-type iterative learning control protocol is proposed. The consensus error between any two agents in the sense of L² norm can converge to zero after enough iterations based on proposed ILC law. And then we extend these conclusions to Lipschitz nonlinear case. Finally, the simulation result shows the effectiveness of the control method.     

Consensus control for multi-agent systems with distributed parameter models via iterative learning algorithm

This paper deals with the problem of iterative learning control algorithm for a class of multi-agent systems with distributed parameter models. And the considered distributed parameter models are governed by the parabolic or hyperbolic partial differential equations. Based on the framework of network topologies, a consensus-based iterative learning control protocol is proposed by using the nearest neighbor knowledge. When the iterative learning control law is applied to the systems, the consensus errors between any two agents on L² space are bounded, and furthermore, the consensus errors on L² space can converge to zero as the iteration index tends to infinity in the absence of initial errors. Simulation examples illustrate the effectiveness of the proposed method.     

Data-driven iterative feedforward control with rational parametrization: Achieving optimality for varying tasks

In precision motion systems, well-designed feedforward control can effectively compensate for the reference-induced error. This paper aims to develop a novel data-driven iterative feedforward control approach for precision motion systems that execute varying reference tasks. The feedforward controller is parameterized with the rational basis functions, and the optimal parameters are sought to be solved through minimizing the tracking error. The key difficulty associated with the rational parametrization lies in the non-convexity of the parameter optimization problem. Hence, a new iterative parameter optimization algorithm is proposed such that the controller parameters can be optimally solved based on measured data only in each task irrespective of reference variations. Two simulation cases are presented to illustrate the enhanced performance of the proposed approach for varying tasks compared to pre-existing results.     

The maximum likelihood least squares based iterative estimation algorithm for bilinear systems with autoregressive moving average noise

Maximum likelihood methods are significant for parameter estimation and system modeling. This paper gives the input-output representation of a bilinear system through eliminating the state variables in it, and derives a maximum likelihood least squares based iterative for identifying the parameters of bilinear systems with colored noises by using the maximum likelihood principle. A least squares based iterative (LSI) algorithm is presented for comparison. It is proved that the maximum of the likelihood function is equivalent to minimize the least squares cost function. The simulation results indicate that the proposed algorithm is effective for identifying bilinear systems and the maximum likelihood LSI algorithm is more accurate than the LSI algorithm.     

Gradient-based iterative identification method for multivariate equation-error autoregressive moving average systems using the decomposition technique

This paper studies the parameter estimation problems of multivariate equation-error autoregressive moving average systems. Firstly, a gradient-based iterative algorithm is presented as a comparison. In order to improve the computational efficiency and the parameter estimation accuracy, a decomposition-based gradient iterative algorithm is presented by using the decomposition technique. The key is to transform an original system into two subsystems and to estimate the parameters of each subsystem, respectively. Compared with the gradient-based iterative algorithm, the decomposition-based algorithm requires less computational efforts, and the simulation results indicate that this algorithm is effective.     

Iterative identification for multiple-input systems with time-delays based on greedy pursuit and auxiliary model

This paper focuses on the identification of multiple-input single-output output-error systems with unknown time-delays. Since the time-delays are unknown, an identification model with a high dimensional and sparse parameter vector is derived based on overparameterization. Traditional identification methods cannot get sparse solutions and require a large number of observations unless the time-delays are predetermined. Inspired by the sparse optimization and the greedy algorithms, an auxiliary model based orthogonal matching pursuit iterative (AM-OMPI) algorithm is proposed by using the orthogonal matching pursuit, and then based on the gradient search, an auxiliary model based gradient pursuit iterative algorithm is proposed, which is computationally more efficient than the AM-OMPI algorithm. The proposed methods can simultaneously estimate the parameters and time-delays from a small number of sampled data. A simulation example is used to illustrate the effectiveness of the proposed algorithms.     

Hierarchical gradient- and least squares-based iterative algorithms for input nonlinear output-error systems using the key term separation

This paper considers the parameter identification problems of the input nonlinear output-error (IN-OE) systems, that is the Hammerstein output-error systems. In order to overcome the excessive calculation amount of the over-parameterization method of the IN-OE systems. Through applying the hierarchial identification principle and decomposing the IN-OE system into three subsystems with a smaller number of parameters, we present the key term separation auxiliary model hierarchical gradient-based iterative algorithm and the key term separation auxiliary model hierarchical least squares-based iterative algorithm, which are called the key term separation auxiliary model three-stage gradient-based iterative algorithm and the key term separation auxiliary model three-stage least squares-based iterative algorithm. The comparison of the calculation amount and the simulation analysis indicate that the proposed algorithms are effective.