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Inequalities in the proof of college entrance examination, often with the series, conics, trigonometric functions,"Three Second," the number of permutation and combination of combining the answers to questions.

高考中证明不等式问题,常常是与数列、二次曲线、三角函数、"三个二次"、排列组合数等相结合的解答题。

This paper presents a new algorithm for feed forward neural networks based on a new optimal target function constructed according to Young inequality in the conjugate of convex function.

提出的算法是利用凸函数共轭性质中的Young不等式构造优化目标函数,这个优化目标函数对于权值和隐层输出来说为凸函数,不存在局部最小。

Generalized strong vector variational-like inequalities; Minty's lemma; KKM mapping; pseudomonotone; Ky Fan lemma; open mapping theorem; complete; the second category; conjugate; proper convex lower semicontinuous function; linear mapping; closedness

基础科学,数学,数学分析广义强向量似变分不等式; Minty引理; KKM映象;伪单调; Ky Fan引理;开映射定理;完备;第二纲;共轭;真凸下半连续函数;线性映射;闭性

By introducing two pairs of conjugate exponent parameters and, and using the improved Euler-Maclaurin's summation formula for estimating the weight coefficient, a reverse Hilbert-type inequality with a best constant factor is proved.

本文通过引入两对共轭指数参量与及应用改进的Euler-Maclaurin求和公式以估算权系数,证明了一个具有最佳常数因子的逆向Hilbert型不等式;作为应用,还给出了相应的等价式及一些特殊结果。

Chapter 4 establishes the theory of convex fuzzy mappings: The concepts, such as Jensen's inequality, positively homogeneous, infimal convolution, right scalar multiplication and convex hull are introduced. The corresponding theorems are demonstrated by using the parametric representations of fuzzy numbers. In anti-fuzzy number space, the conjugate mapping of convex fuzzy mapping is concerned, and convexities of conjugate set and conjugate mapping of convex fuzzy mapping are proved. The notions of subgradient, subdifferential, differential with respect to convex fuzzy mappings are investigated, which provides the basis of the theory of fuzzy extremum problems.

在第4章中,建立了有关凸模糊映射的理论:建立了关于凸模糊映射的Jensen不等式、模糊正齐次映射、凸模糊映射的下卷积、右数乘和凸包等概念,利用模糊数的参数化表示,给出了相应的定理;在反模糊数空间,对凸模糊映射的共轭也作了探讨,证明了凸模糊映射的共轭集合和共轭映射都是凸的;最后对凸模糊映射的次梯度、次微分和微分等概念进行了研究,为模糊极值理论打下了基础。

It takesthe weighted average of the L2 norm of the difference of the observation and thesolution of the system and the L2 norm of the difference of conormal derivativeat the different sides of the interface for every subdomain as cost functional andthe smooth coefficients of the subproblem and the value of solution of the originalproblem at interface as identification parameters;Using the property of continu-ous functional defined on compact set,the existence of the optimal solution of theidentification problem is proved;The necessary conditions of optimality charac-terized by the system equation,the adjoit equation and the variational inequalitysimultaneously are given by introducing the conception ofdifferential andadjoit variable;An algorithm is devised and its flow graph is given.

其次,针对分片光滑动力系统的特征,结合正演过程的区域分解算法,建立了分片光滑系统的分解区域参数辨识模型,该模型以子区域上解的实测值与计算值之差的L2范数和界面两侧的通量差的L2范数的加权平均作目标泛函,各子问题的光滑系数及界面上真解的值为待辨识参量;利用紧致集上连续泛函的性质,证明了子区域上参数辨识问题最优辨识参量的存在性;引入微分的概念,借助伴随变量,给出了由系统方程,伴随方程和变分不等式共同表征的最优性必要条件;根据此必要条件设计了算法,给出了算法的程序框图。

In order to reduce the design conservativeness, a condition for the existence of a parameter-dependent separator is proposed, and further the filter design can be converted into the optimization problem involving linear matrix inequalities.

为进一步降低设计的保守性,文中还给出了一种依参数分离算子的滤波器存在条件,并将滤波器设计转化为线性矩阵不等式表述的凸优化问题。

The merit of the proposed condition lies in their less conservativeness.

该判据用线性矩阵不等式的形式给出,其优点在于降低了现有文献结论的保守性。

Under a continuous fault model of the actuator,the consistency conditions of two indices fault-tolerant performance are set up by linear matrix inequality approach,and the ranges of constraint indices are analyzed.

在连续型执行器故障的模式下,利用线性矩阵不等式技术,给出了2个指标约束下容错性能的相容性判别条件,分析了与区域极点指标相容的H∞指标的取值范围,并在相容指标约束下给出了满意容错控制器的设计方法。

We mainly use the barrier functions, the interpolation inequality, a prior estimate and continuity method in this thesis.

本文所用的主要方法,包括构造闸函数,插值不等式,先验估计以及连续性方法。

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My dream is to be a crazy growing tree and extend at the edge between the city and the forest.

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单击"保存"会将文件保存到主持人的硬盘或服务器上,而不是您自己的计算机上。