2003年第四期初中数学问题解答

一、分解因式 :6x2 -5xy-4y2 -1 1x 2 2y -1 0 .解 :注意到 6x2 -5xy -4y2 =( 2x y) ( 3x -4y) .设 6x2 -5xy -4y2 -1 1x 2 2y-1 0=( 2x y k) ( 3x -4y l) ,则 6x2 -5xy -4y2 -1 1x 2 2y-1 0=6x2 -5xy -4y2 ( 3k 2l)x ( -4k l)y kl.比较对应项的系数得 :3k 2l=-1 1 ,-4k l=2 2 ,kl=-1 0 .　　解得 k =-5 ,l=2 .于是 6x2 -5xy -4y2 -1 1x 2 2y-1 0　 =( 2x y -5 ) ( 3x -4y 2 ) .二、求函数y =|x2 -4|-3x在区间 -2≤x≤ 5中的最大值和最小值 ,并求当y为最大值时的x值 .解 :若x2 -4≥ 0 ,即 |x|≥ 2 ,则　　y=x2 -3x-4=(x-32 ) 2 -2 54.当 |x|≤ 2时 ,　　y=-x2 -3x 4 =-(x 32 ) 2 2 54.从而求得 :当x=-32 时 ,y最大值 =2 54;当x=...     

对《巧求值》的指正和另解

<正>《中学生数学》2013年第6期(下)刊载的《智慧窗》第三题《巧求值》及"解答"如下:题目已知:x2-4x-3=0,y2-6y+2=0,求x2+2y2+4x-4y-2的值.参考答案将x2-4x-3=0化为(x+2)2-8(x+2)+9=0.将y2-6y+2=0化为(y+1)2-8(y+1)+9=0.因此,x+2,y+1是一元二次方程t2-8t+9=0的两个根.     

对一道竞赛试题解法的一点看法

20 0 2年全国高中数学联合竞赛有一道平面解析几何试题 ,试题及参考答案如下 :图 1题　如图 1 ,已知点A( 0 ,2 )和抛物线 y2 =x + 4上两点 B,C,使得 AB⊥ BC,求点 C的纵坐标的取值范围 .解　设 B点坐标为 ( y21- 4,y1) ,C点坐标为 ( y2 -4,y) ,　　显然 y21- 4≠ 0 ,故 k AB =y1- 2y21- 4=1y1+ 2 .由于 AB⊥ BC,所以 k BC =- ( y1+ 2 ) ,从而y - y1=- ( y1+ 2 ) [x - ( y21- 4) ],y2 =x + 4 .消去 x,注意到 y≠ y1得 :( 2 + y1) ( y + y1) + 1 =0 ,y21+ ( 2 + y) y1+ ( 2 y + 1 ) =0 .由Δ≥ 0解得　 y≤ 0或 y≥ 4 .当 y =0时 ,点 …     

北京数学奥林匹克学校课堂实录

课　题　整式与分式的求值适用年级　初中二年级学期　 2 0 0 3— 2 0 0 4学年度第一学期训练目的典型范例　　已知x + y=1,x2 + y2 =2 ,求x7+ y7的值 .分析与解答　所求式与已知式关系甚远 ,考虑添设辅助式 (中间元素 )x3+ y3与x4 + y4 ,沟通它们之间的联系 ,缩短它们之间的距离 .分析与解　∵　x +y =1,　x2 +y2 =2 ,∴　xy =12 [(x + y) 2 -(x2 + y2 ) ]=12 (12 -2 ) =-12 ,x3+y3=(x +y) (x2 -xy + y2 )=1× (2 + 12 ) =52 ,x4 + y4 =(x2 + y2 ) 2 -2x2 y2=2 2 -2 (-12 ) 2 =72 .故　x7+ y7=(x3+ y3) (x4 + y4 ) -(x4 y3+x3y4 )=(x3+ y3)…     

二元函数极值的一种新判别方法

通常都是利用二阶偏导数来判别二元函数 z =f (x,y)的极值存在性 .本文将讨论如何利用一阶偏导数来判别二元函数的极值存在性 .我们知道 ,在利用二阶偏导数判别 z =f (x,y)的极值时存在着两方面的不便 :1°要计算三个二阶偏导数值 ;2°当 [fxx .fyy -f2xy]( x0 ,y0 ) =0时 ,不能确定极值是否存在 .下面我们受一元函数极值判别的启发 ,利用一元函数的性质 ,研究如何用一阶偏导数判别二元函数的极值 .设二元函数 z =f (x,y)在点 (x0 ,y0 )的 δ-邻域 B| ( x0 ,y0 ) ={ (x,y) | 0 <(x -x0 ) 2 (y -y0 ) 2 <δ}内有连续偏导数 ,(x,y)是该邻域…     

新老教材习题一例浅探

在新旧教材中都有这样一道题目 :问题 1　求经过两圆 x2 y2 6 x - 4=0和 x2 - y2 6 y - 2 8=0的交点 ,并且圆心在直线 x - y - 4=0上的圆的方程 (老教材《平面解析几何》全一册 (必修 ) P70 ;新教材《数学》第二册 (上 ) (试验修订本 .必修 ) P82 ) .此题一般有两种解法 .简解 1　将两圆的方程联立解得x =- 1 ,y =3. 　或　 x =- 6 ,y =- 2 .知两圆的交点为 A( - 1 ,3) ,B( - 6 ,- 2 ) .而圆心在线段 AB的垂直平分线 x y 3=0上 ,由 x y 3=0 ,x - y - 4=0 .　 解得圆心坐标为C( 12 ,- 72 ) ,又圆的半径为 r =| AC| =12 1 7…     

世界各地数学奥林匹克竞赛试题摘编

1　 (第 2 3届全俄中学奥林匹克竞赛试题 ,11年级 )求方程 (x2 - y2 ) 2 =1+ 16 y的整数解 .解　以下将证明方程(x2 - y2 ) 2 =1+ 16 y (1)的解是 (- 4,5 ) ,(4,5 ) ,(- 1,0 ) ,(1,0 ) ,(- 4,3) ,(4,3) .设x ,y是满足方程 (1)的两个整数 .注意到 ,若 y <0 ,则 1+ 16 y <0 ,则 1+ 16 y不是一个完全平方数 ;若 (x ,y)就是 (1)的解 .不失一般性 ,可设x≥ 0 .情形 1:若x≥y ,可令x =y +a且a∈N .方程 (1)可改写为 :4a2 y2 + 4 (a3- 4) y +a4 - 1=0 .故 y是二次方程 4a2 X2 + 4 (a3- 4)X +a4 - 1=0的一个解 .此时Δ =16 (- 8a3+a2 + 16 ) ,则一…     

新题征展(57)

A 题组新编 1.已知圆x2 y2-2x 4y 1=0和直线2x y c=0.     

2007年普通高等学校招生全国统一考试 (天津卷)数学(理工类)

一、选择题:共10个小题,满分50分.1.i是虚数单位,12-i3i=()A.1 iB.-1 iC.1-iD.-1-i2.设变量x,y满足约束条件x-y≥-1,x y≥1,3x-y≤3,则目标函数z=4x y的最大值为()A.4B.11C.12D.143.“θ=23π”是“tanθ=2cos2π θ”的()A.充分而不必要条件B.必要而不充分条件C.充分必要条件D.既不充分也不必要条件4.设双曲线xa22-by22=1(a>0,b>0)的离心率为3,且它的一条准线与抛物线y2=4x的准线重合,则此双曲线的方程为()A.1x22-2y42=1B.4x82-9y62=1C.x32-23y2=1D.x32-y62=15.函数y=log2x 4 2(x>0)的反函数是()A.y=4x-2x 1(x>2)B.y=4x-2x 1(x>1)C…     

不等式研究成果集锦(18)

21 3　设 x1,x2 ,x3 ∈ R,且 0 0 ,y1 y2 y3 >0 ,x1x2 x2 x3 x3 x1>0 ,y1y2 y2 y3 y3 y1>0 ,则有∑(x1 y1) (x2 y2 )≥ ∑x1x2 ∑y1y2 .(陈天华 .2 0 0 1 ,2 )2 1 5　若 { am} ,{ bn}是两个非负实数列 ,且定义 Am =1m∑mi=1ai,Bn =1n∑ni=1bi,对于m =1 ,2 ,… ,k;…     

压裂效果预测方法研究

预测油井压裂效果对实施增产措施决策意义重大.应用科学的数学统计方法建立了大庆油田采油二厂某区块压裂措施库,采用灰色关联方法分析影响压裂效果的主要因素,应用多元回归方法、逐步回归方法、人工组合回归方法分别建立预测模型,进而建立起适用于不同压前含水和液量级别的预测图版,增油量绝对误差在1.5t以内的井的符合率达到77.4%.方法可操作性强,预测符合率较高,可用于指导优化压裂工艺,提高剩余储量动用程度.     

多目标预测模型在黑龙江垦区大豆产量预测中的应用

在预测模型的均值和稳定性基础上,建立了多目标组合优化模型,并以黑龙江九三地区35年的大豆产量数据为例,利用该地区大豆单产的Logistic模型和大豆产量与气象因子的逐步回归模型建立了多目标组合优化模型,并计算出最优解.结果表明,该组合模型没有最优点,而有非劣解.该方法对提高模型的精度,指导大豆生产具有重要意义.     

组合预测方法在黑龙江垦区大豆产量预测中的应用

首先建立了黑龙江垦区九三地区的大豆单产的Logistic模型,通过梯度法得出模型的参数估计值,并对模型进行了分析.其次,再利用逐步回归方法建立了大豆产量与气象因子的逐步回归模型,确定了影响九三地区大豆产量的主要气象因子,最后运用组合预测和最优权数计算的方法,建立了九三地区大豆产量的组合预测模型,进一步提高了模型的精度,这对指导大豆生产具有重要意义.     

Short Term Forecasting: Horses for Courses

Within the British Gas Corporation the Headquarters Operational Research Department has assisted various clients with a range of short term forecasting problems. As a result, over the years considerable experience has been gained with the use of alternative forecasting procedures. The authors felt that it would be interesting to compare the usefulness of these alternative approaches, not as usual by their forecasting performance measured in terms of some statistical parameter, but rather in terms of some qualitative factors. Amongst these we identify the objectives of the forecaster, nature of the environment, ease of use and nature of the time series itself. Such factors we believe may well be more important than the forecasting performance as determined by strictly statistical measures. This paper discusses, in these terms, our experience of using alternative forecasting models for appliance sales and gas sendout forecasting problems.     

The impact of special days in call arrivals forecasting: A neural network approach to modelling special days

A key challenge for call centres remains the forecasting of high frequency call arrivals collected in hourly or shorter time buckets. In addition to the complex intraday, intraweek and intrayear seasonal cycles, call arrival data typically contain a large number of anomalous days, driven by the occurrence of holidays, special events, promotional activities and system failures. This study evaluates the use of a variety of univariate time series forecasting methods for forecasting intraday call arrivals in the presence of such outliers. Apart from established, statistical methods, we consider artificial neural networks (ANNs). Based on the modelling flexibility of the latter, we introduce and evaluate different methods to encode the outlying periods. Using intraday arrival series from a call centre operated by one of Europe’s leading entertainment companies, we provide new insights on the impact of outliers on the performance of established forecasting methods. Results show that ANNs forecast call centre data accurately, and are capable of modelling complex outliers using relatively simple outlier modelling approaches. We argue that the relative complexity of ANNs over standard statistical models is offset by the simplicity of coding multiple and unknown effects during outlying periods.     

基于贝叶斯向量自回归的区域经济预测模型:以青海为例

由于缺乏足够的观测数据等原因,常规的区域经济预测模型在我国难以获得预期的预测效果,而贝叶斯向量自回归(BVAR)模型将变量的统计性质作为参数的先验分布引入到传统的VAR模型中,能够克服自由度过少的问题,以青海为例,本文建立了一个BVAR模型,并引入了全国GDP和中央政府转移支付作为外生变量以描述国民经济与区域经济的联系...     

An application of support vector machines to sales forecasting under promotions

This paper deals with sales forecasting of a given commodity in a retail store of large distribution. For many years statistical methods such as ARIMA and Exponential Smoothing have been used to this aim. However the statistical methods could fail if high irregularity of sales are present, as happens for instance in case of promotions, because they are not well suited to model the nonlinear behaviors of the sales process. In recent years new methods based on machine learning are being employed for forecasting applications. A preliminary investigation indicates that methods based on the support vector machine (SVM) are more promising than other machine learning methods for the case considered. The paper assesses the application of SVM to sales forecasting under promotion impacts, compares SVM with other statistical methods, and tackles two real case studies.     

Health care systems modeling at IIASA: A status report: E.N. SHIGAN,D.J. HUGHES and P.I. KITSUL IIASA,Laxenburg, Austria, 1979, x + 65 pages; Available free of charge from the IIASA Publication Department

A comparison of several methods for estimating the observation and process noise variances in the Kalman filtering approach to statistical forecasting is undertaken by means of simulated time series analysis. Particular attention is given to the requirement of an online, automatic, forecasting system for recursive estimation procedures.     

Tactical sales forecasting using a very large set of macroeconomic indicators

Tactical forecasting in supply chain management supports planning for inventory, scheduling production, and raw material purchase, amongst other functions. It typically refers to forecasts up to 12 months ahead. Traditional forecasting models take into account univariate information extrapolating from the past, but cannot anticipate macroeconomic events, such as steep increases or declines in national economic activity. In practice this is countered by using managerial expert judgement, which is well known to suffer from various biases, is expensive and not scalable. This paper evaluates multiple approaches to improve tactical sales forecasting using macro-economic leading indicators. The proposed statistical forecast selects automatically both the type of leading indicators, as well as the order of the lead for each of the selected indicators. However as the future values of the leading indicators are unknown an additional uncertainty is introduced. This uncertainty is controlled in our methodology by restricting inputs to an unconditional forecasting setup. We compare this with the conditional setup, where future indicator values are assumed to be known and assess the theoretical loss of forecast accuracy. We also evaluate purely statistical model building against judgement aided models, where potential leading indicators are pre-filtered by experts, quantifying the accuracy-cost trade-off. The proposed framework improves on forecasting accuracy over established time series benchmarks, while providing useful insights about the key leading indicators. We evaluate the proposed approach on a real case study and find 18.8% accuracy gains over the current forecasting process.     

Fuzzy interaction regression for short term load forecasting

Electric load forecasting is a fundamental business process and well-established analytical problem in the utility industry. Due to various characteristics of electricity demand series and the business needs, electric load forecasting is a classical textbook example and popular application field in the forecasting community. During the past 30 plus years, many statistical and artificial intelligence techniques have been applied to short term load forecasting (STLF) with varying degrees of success. Although fuzzy regression has been tried for STLF for about a decade, most research work is still focused at the theoretical level, leaving little value for practical applications. A primary reason is that inadequate attention has been paid to the improvement of the underlying linear model. This application-oriented paper proposes a fuzzy interaction regression approach to STLF. Through comparisons to three models (two fuzzy regression models and one multiple linear regression model) without interaction effects, the proposed approach shows superior performance over its counterparts. This paper also offers critical comments to a notable but questionable paper in this field. Finally, tips for practicing forecasting using fuzzy regression are discussed.