毛细管电色谱中进样时间对柱效的影响

毛细管电色谱是近年发展起来的一种新型微分离技术，它结合了高效液相色谱和毛细管电泳的物点，具有高效、快速，高选择性，其有关理论研究和应用越来越深入，本文从色谱动力学角度讨论了毛细管电色谱中进样时间对柱的影响。     

光泽精化学发光体系的研究与应用

对光泽精化学发光体系的动力学性质、荧光光谱、化学发光光谱及化学发光机理进行研究和探讨。详细研究了光泽精 - H2 O2 - Co( )流动注射化学发光体系的最佳分析条件。结果表明 ,当光泽精 (L c)为 2 .5×10 - 5mol/L、H2 O2 为 0 .5 mol/L、Na OH为 0 .4 mol/L、CTMAB为 2 .0× 10 - 5mol/L时 ,测定 Co( )的校准曲线线性范围为 0 .0 5— 2 0 0μg/m L ,检出限为 0 .0 1μg/m L。对 10μg/m L Co( )连续测定 7次 ,相对标准偏差为 1.7%。     

Constraint incorporation in optimization

Numerical methods for solving constrained optimization problems need to incorporate the constraints in a manner that satisfies essentially competing interests; the incorporation needs to be simple enough that the solution method is tractable, yet complex enough to ensure the validity of the ultimate solution. We introduce a framework for constraint incorporation that identifies a minimal acceptable level of complexity and defines two basic types of constraint incorporation which (with combinations) cover nearly all popular numerical methods for constrained optimization, including trust region methods, penalty methods, barrier methods, penalty-multiplier methods, and sequential quadratic programming methods. The broad application of our framework relies on addition and chain rules for constraint incorporation which we develop here.     

Feature Selection using Fuzzy Support Vector Machines

The feature selection consists of obtaining a subset of these features to optimally realize the task without the irrelevant ones. Since it can provide faster and cost-effective learning machines and also improve the prediction performance of the predictors, it is a crucial step in machine learning. The feature selection methods using support machines have obtained satisfactory results, but the noises and outliers often reduce the performance. In this paper, we propose a feature selection approach using fuzzy support vector machines and compare it with the previous work, the results of experiments on the UCI data sets show that feature selection using fuzzy SVM obtains better results than using SVM.     

搜索三个目标

本文通过建立简洁而有效的搜索方法 ,证明了对于无穷多个n来说 ,三个目标的最优搜索问题的最小试验次数等于信息论下界 .同时也研究了上述问题的修正情形 ,证明了对于所有整数n来说 ,对应的最小试验次数或者等于信息论下界或者超过信息论下界 1次并且对于无穷多个区间 ,信息论下界均是可以达到的 .     

无严格互补松驰条件的序列线性方程组新算法

该文通过构造特殊形式的有效集来逼近KKT点处的有效集，给出了一个任意初始点下的序列线性方程组新算法，并证明了该算法在没有严格互补松驰条件的情况下具有全局收敛性和一步超线性收敛性。      

概率罐子模型中一种最优设计

基于错误概率达到最小的原则，该文在罐子模型序贯试验中构造了一种渐近最优设计。在这种设计下，不仅能使病员以较多机会分配到较好的处理，而且能使估计量的统计效率在一定意义下达到最优。     

SEQUENTIAL QUADRATIC PROGRAMMING METHODS FOR OPTIMAL CONTROL PROBLEMS WITH STATE CONSTRAINTS

A Kind of direct methods is presented for the solution of optimal control problems with state constraints.These methods are sequential quadratic programming methods.At every iteration a quadratic programming which is obtained by quadratic approximation to Lagrangian function and Linear approximations to constraints is solved to get a search direction for a merit function.The merit function is formulated by augmenting the Lagrangian funetion with a penalty term.A line search is carried out along the search direction to determine a step length such that the merit function is decreased.The methods presented in this paper include continuous sequential quadratic programming methods and discreate sequential quadrade programming methods.     

On the multi-attribute stopping problem with general value functions

We examine a sequential selection problem in which a single option must be selected. Each option's value is a function of its attributes, whose precise values can be ascertained at a given cost. We prove the optimality of a threshold stopping rule for a general class of objective functions.