NOTES ON GENERALIZED EXPONENTIAL DICHOTOMIES

ＮＯＴＥＳＯＮＧＥＮＥＲＡＬＩＺＥＤＥＸＰＯＮＥＮＴＩＡＬＤＩＣＨＯＴＯＭＩＥＳ￥ＺＨＡＮＧＷＥＩＮＩＡＮ（张伟年）（ＣｅｎｔｅｒｆｏｆＭａｔｈｅｍａｔｉｃａｌＳｃｉｅｎｃｅｓＣＩＣＡ，ｔｈｅＣｈｉｎｅｓｅＡｃａｄｅｍｙｏｆＳｃｉｅｎｃｅｓ，Ｃｈｅｎ...     

WELL-BEHAVED BASIS AND LR ARRAYS

ＷＥＬＬ－ＢＥＨＡＶＥＤＢＡＳＩＳＡＮＤＬＲＡＲＲＡＹＳＬＩＮＤＯＮＧＤＡＩ（林东岱）（ＩｎｓｔｉｔｕｔｅｏｆＳｙｓｔｅｍｓＳｃｉｅｎｃｅ，ｔｈｅＣｈｉｎｅｓｅＡｃａｄｅｍｙｏｆＳｃｉｅｎｃｅ，Ｂｅｉｊｉｎｇ１０００８０，Ｃｈｉｎａ）Ａｂｓｔｒａｃｔ...     

BFEEDBACK CONTROL OF SINGLE-LINK FLEXIBLE ARMS

ＦＥＥＤＢＡＣＫＣＯＮＴＲＯＬＯＦＳＩＮＧＬＥ－ＬＩＮＫＦＬＥＸＩＢＬＥＡＲＭＳＦＥＮＧＤＥＸＩＮＧ（冯德兴）；ＺＨＡＮＧＷＥＩＴＡＯ（张维韬）（ＩｎｓｔｉｔｕｔｅｏｆＳｙｓｔｅｍｓＳｃｉｅｎｃｅ，ｔｈｅＣｈｉｎｅｓｅＡｃａｄｅｍｙｏｆＳｃｉｅｎｃｅ...     

MARKOV DECISION PROGRAMMING WITH CONSTRAINTS

ＭＡＲＫＯＶＤＥＣＩＳＩＯＮＰＲＯＧＲＡＭＭＩＮＧＷＩＴＨＣＯＮＳＴＲＡＩＮＴＳＬＩＵＪＩＡＮＹＯＮＧ（刘建庸）；ＬＩＵＫＥ（刘克）（ＩｎｓｔｉｔｕｔｅｏｆＡｐｐｌｉｅｄＭａｔｈｅｍａｔｉｃｓ，ｔｈｅＣｈｉｎｅｓｅＡｃａｄｅｍｙｏｆＳｃｉｅｎｃｅｓ，...     

On Bahadur asymptotic efficiency in a semiparametric regression model

ＯＮＢＡＨＡＤＵＲＡＳＹＭＰＴＯＴＩＣＥＦＦＩＣＩＥＮＣＹＩＮＡＳＥＭＩＰＡＲＡＭＥＴＲＩＣＲＥＧＲＥＳＳＩＯＮＭＯＤＥＬＬＩＡＮＧＨＵＡ（梁华）;ＣＨＥＮＧＰＩＮＧ（成平）（ＩｎｓｔｉｔｕｔｅｏｆＳｙｓｔｅｍｓＳｃｉｅｎｃｅ,ｔｈｅＣｈｉｎｅｓｅＡ...     

On upper bounds of bandwidths of trees

ＯＮＵＰＰＥＲＢＯＵＮＤＳＯＦＢＡＮＤＷＩＤＴＨＳＯＦＴＲＥＥＳＷＡＮＧＪＩＡＮＦＡＮＧ（王建方）（ＩｎｓｔｉｔｕｔｅｏｆＡｐｐｌｉｅｄＭａｔｈｅｍａｔｉｃｓ,ｔｈｅＣｈｉｎｅｓｅＡｃａｄｅｍｙｏｆＳｃｉｅｎｃｅｓ,Ｂｅｉｊｉｎｇ１０００８０,Ｃｈｉ...     

AN INVARIANT FOR HYPERGRAPHS

ＡＮＩＮＶＡＲＩＡＮＴＦＯＲＨＹＰＥＲＧＲＡＰＨＳＷＡＮＧＪＩＡＮＦＡＮＧ（ＩｎｓｔｉｔｕｔｅｏｆＡＰＰｌｉｅｄＭａｔｈｅｍａｔｉｃｓ，ＣｈｉｎｅｓｅＡｃａｄｅｍｙｏｆＳｃｉｅｎｃｅｓ，Ｂｅｉｊｉｎｇ１０００８０，ＣｈｉｎａａｎｄＡｓｉａ－Ｐａｃｉｆ...     

THE NBELC AND NWELC CLASSES OF LIFE DISTRIBUTIONS

ＴＨＥＮＢＥＬＣＡＮＤＮＷＥＬＣＣＬＡＳＳＥＳＯＦＬＩＦＥＤＩＳＴＲＩＢＵＴＩＯＮＳ￥ＣＡＯＪＩＮＨＵＡ；ＷＡＮＧＹＵＥＤＯＮＧ（ＩｎｓｔｉｔｕｔｅｏｆＡｐｐｌｉｅｄＭａｔｈｅｍａｔｉｃｓ，ＣｈｉｎｅｓｅＡｃａｄｅｍｙｏｆＳｃｉｅｎｃｅ，Ｂｅｉｊｉｎ...     

Reliability bounds in DFRA class with known mean and variance

ＲＥＬＩＡＢＩＬＩＴＹＢＯＵＮＤＳＩＮＤＦＲＡＣＬＡＳＳＷＩＴＨＫＮＯＷＮＭＥＡＮ　ＡＮＤ　ＶＡＲＩＡＮＣＥＣＨＥＮＧＫＡＮ（程侃）（ＩｎｓｔｉｔｕｔｅｏｆＡｐｐｌｉｅｄＭａｔｈｅｍａｔｉｃｓ,ｔｈｅＣｈｉｎｅｓｅＡｃａｄｅｍｙｏｆＳｃｉｅｎｃｅ,Ｂ...     

A survey of orthogonal arrays of strength two

ＡＳＵＲＶＥＹＯＦＯＲＴＨＯＧＯＮＡＬＡＲＲＡＹＳＯＦＳＴＲＥＮＧＴＨＴＷＯＬＩＵＺＨＡＮＧＷＥＮ（刘璋温）（ＩｎｓｔｉｔｕｔｅｏｆＡｐｐｌｉｅｄＭａｔｈｅｍａｔｉｃｓ．ｔｈｅＣｈｉｎｅｓｅＡｃａｄｅｍｙｏｆＳｃｉｅｔｉｃｅｓ．Ｂｅｉｊｉｎｇ１０００...     

一个新的解线性规划的神经网络

本文提出一个新的解线性规划的Hopfields-型网络。该网络基于线性规划的对偶理论,并使用了Sigmoid函数,但不需要预先给定的罚参数和乘法模拟器,我们证明该网络不仅全局收敛到线性规划的精确解,而且能同时解原规划和对偶规划。由于在该网络中没有使用乘法模拟器而利用了Sigmoid函数,因此该模型是很容易用硬件实现的。     

TRUST REGION METHOD IN NEURAL NETWORK

1.IntroductionHopfieldandTank[5]presentedamodeltosolvetravellingsalesmanproblem,thusinitiatingtheapplicationofneuralnetwork(NN)inthefieldofoptimization.SincethenmanyNNmodelshavebeenproposedtosolvelinearprogramming(LP)problems(13,8,11,14,15])andquadraticprogramming(oP)problems([1,8,20]),asLPandoPhavefundamentalimportanceinthetheoryandpracticeofoptimization.Therewerealsoafewmodelsforgeneralnonlinearprogramming(NP)problem([2,6,9,18]).InthispaperwewillpresentaHopfield-typeneuralnetworkmodelw…     

A branch-and-bound algorithm for 0–1 parametric mixed integer programming

A branch-and-bound algorithm to solve 0–1 parametric mixed integer linear programming problems has been developed. The present algorithm is an extension of the branch-and-bound algorithm for parametric analysis on pure integer programming. The characteristic of the present method is that optimal solutions for all values of the parameter can be obtained.     

约束非线性规划的神经网络算法

神经网络具有内在大规模并行运算和快速收敛特性,它在最优化技术上的运用近年来受到广泛的重视。本提出一个新的求解一般约束非线性规划的神经网络模型,它具有全局收敛性和广泛的适用性,是求解一般非线性规划问题的新工具。理论分析和模拟计算均表明了模型的有效性。     

求解线性约束的区间二次规划问题的神经网络

在本文中,基于神经网络,提出了一类求解具有线性约束区间二次规划问题的方法,使用增广拉格朗日函数,建立了求解规划问题的神经网络模型。基于压缩不动点理论,证明了所提出神经网络的平衡点就是等式约束区间二次规划问题的最优解。使用适当的Lyapunov函数,证明了所提出的神经网络的平衡点是全局指数稳定的。最后,两个数值仿真结果验证了本文所用方法的可行性与有效性。     

An Adaptive Neural Network Model for Nonlinear Programming Problems

In this paper a canonical neural network with adaptively changing synaptic weights and activation function parameters is presented to solve general nonlinear programming problems. The basic part of the model is a sub-network used to find a solution of quadratic programming problems with simple upper and lower bounds. By sequentially activating the sub-network under the control of an external computer or a special analog or digital processor that adjusts the weights and parameters, one then solves general nonlinear programming problems. Convergence proof and numerical results are given.     

Extensions of Dinkelbach's algorithm for solving non-linear fractional programming problems

Dinkelbach's algorithm was developed to solve convex fractinal programming. This method achieves the optimal solution of the optimisation problem by means of solving a sequence of non-linear convex programming subproblems defined by a parameter. In this paper it is shown that Dinkelbach's algorithm can be used to solve general fractional programming. The applicability of the algorithm will depend on the possibility of solving the subproblems. Dinkelbach's extended algorithm is a framework to describe several algorithms which have been proposed to solve linear fractional programming, integer linear fractional programming, convex fractional programming and to generate new algorithms. The applicability of new cases as nondifferentiable fractional programming and quadratic fractional programming has been studied. We have proposed two modifications to improve the speed-up of Dinkelbachs algorithm. One is to use interpolation formulae to update the parameter which defined the subproblem and another truncates the solution of the suproblem. We give sufficient conditions for the convergence of these modifications. Computational experiments in linear fractional programming, integer linear fractional programming and non-linear fractional programming to evaluate the efficiency of these methods have been carried out.     

正态随机规划的确定性规划及其灵敏度分析

在大量的管理决策问题中,经常会遇到目标函数的系数和右端常数为相互独立的正态随机变量的随机线性规划模型.利用对偶规划将正态随机规划化为具有α可靠度的线性规划,给出了解决该正态随机规划的一个有效方法,并对正态随机变量的参数进行了灵敏度分析,避免了由于参数估计偏差给决策带来的风险,保证了最优方案的α可靠度.     

模糊规划的Hopfield网络方法

首先将一类模糊规划转化为无约束多目标规划，再依据决策者偏好并采用Hopfield网络方法构造该多目标规划的评价函数，从而将模糊规划转化为无约束单目标规划来求解．