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51.
Ant Colony Optimisation for Machine Layout Problems 总被引:1,自引:0,他引:1
Flexible machine layout problems describe the dynamic arrangement of machines to optimise the trade-off between material handling and rearrangement costs under changing and uncertain production environments. A previous study used integer-programming techniques to solve heuristically reduced versions of the problem. As an alternative, this paper introduces an ant colony optimisation (ACO) algorithm to generate good solutions. Experimental results are presented, with ACO obtaining better solutions than the reduction heuristic. 相似文献
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Rudolf Beran 《Annals of the Institute of Statistical Mathematics》2002,54(4):900-917
Estimation of the mean function in nonparametric regression is usefully separated into estimating the means at the observed factor levels—a one-way layout problem—and interpolation between the estimated means at adjacent factor levels. Candidate penalized least squares (PLS) estimators for the mean vector of a one-way layout are expressed as shrinkage estimators relative to an orthogonal regression basis determined by the penalty matrix. The shrinkage representation of PLS suggests a larger class of candidate monotone shrinkage (MS) estimators. Adaptive PLS and MS estimators choose the shrinkage vector and penalty matrix to minimize estimated risk. The actual risks of shrinkage-adaptive estimators depend strongly upon the economy of the penalty basis in representing the unknown mean vector. Local annihilators of polynomials, among them difference operators, generate penalty bases that are economical in a range of examples. Diagnostic techniques for adaptive PLS or MS estimators include basis-economy plots and estimates of loss or risk. 相似文献
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Rodolf Beran 《Annals of the Institute of Statistical Mathematics》2005,57(2):201-220
The balanced complete multi-way layout with ordinal or nominal factors is a fundamental data-type that arises in medical imaging,
agricultural field trials, DNA microassays, and other settings where analysis of variance (ANOVA) is an established tool.
ASP algorithms weigh competing biased fits in order to reduce risk through variance-bias tradeoff. The acronym ASP stands
for Adaptive Shrinkage of Penalty bases. Motivating ASP is a penalized least squares criterion that associates a separate
quadratic penalty term with each main effect and each interaction in the general ANOVA decomposition of means. The penalty
terms express plausible conjecture about the mean function, respecting the difference between ordinal and nominal factors.
Multiparametric asymptotics under a probability model and experiments on data elucidate how ASP dominates least squares, sometimes
very substantially. ASP estimators for nominal factors recover Stein's superior shrinkage estimators for one- and two-way
layouts. ASP estimators for ordinal factors bring out the merits of smoothed fits to multi-way layouts, a topic broached algorithmically
in work by Tukey.
This research was supported in part by National Science Foundation Grants DMS 0300806 and 0404547. 相似文献
56.
The single row facility layout problem (SRFLP) is the problem of arranging n departments with given lengths on a straight line so as to minimize the total weighted distance between all department pairs. We present a polyhedral study of the triplet formulation of the SRFLP introduced by Amaral [A.R.S. Amaral, A new lower bound for the single row facility layout problem, Discrete Applied Mathematics 157 (1) (2009) 183-190]. For any number of departments n, we prove that the dimension of the triplet polytope is n(n−1)(n−2)/3 (this is also true for the projections of this polytope presented by Amaral). We then prove that several valid inequalities presented by Amaral for this polytope are facet-defining. These results provide theoretical support for the fact that the linear program solved over these valid inequalities gives the optimal solution for all instances studied by Amaral. 相似文献
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Optimizing the initial setting of complex adaptive systems–optimizing the layout of initial AFVs stations for maximizing the diffusion of AFVs 下载免费PDF全文
There are occasions when people want to optimize the initial setting of a CAS (complex adaptive system) so that it evolves in a desired direction. A CAS evolves by heterogeneous actors interacting with each other. It is difficult to describe the evolution process with an objective function. Researchers usually attempt to optimize an intervening objective function, which is supposed to help a CAS evolve in a desired direction. This article puts forward an approach to optimize the initial setting of a CAS directly (instead of through an intervening objective function) by nesting agent‐based simulations in a genetic algorithm. In the approach, an initial setting of a CAS is treated as a genome, and its fitness is defined by the closeness between the simulation result and the desired evolution. We test the applicability of the proposed approach on the problem of optimizing the layout of initial AFV (alternative fuel vehicle) refueling stations to maximize the diffusion of AFVs. Computation experiments show that the initial setting generated with the approach could better induce the desired evolving result than optimizing an intervening objective function. The idea of the approach can also be applied to other decision making associated with a complex adaptive process. © 2015 Wiley Periodicals, Inc. Complexity 21: 275–290, 2016 相似文献
59.
基于第十一届"华为杯"全国研究生数学建模竞赛E题第五问,针对一类多车型多目的地的整车物流运输调度问题,先直接计算完成总任务所需的车辆数来阐明该题的最优解的下界限为113辆,再对原始数据进行预处理,基于对乘用车的分类与排样算法,筛选出每种轿运车的M种装载方案代表,再对目的地位置及结合各目的地的任务需求,确定出3条不绕行路线,根据启发式调整优化算法,并以轿运车使用量最少及总行驶里程最短为优化目标,建立了多目标整数规划模型进行求解,最优可行解为114辆,其中1-1型91辆,1-2型18辆,2-2型5辆. 相似文献
60.
The Model for Two-dimensional Layout Optimization Problem with Performance Constraints and Its Optimality Function 总被引:1,自引:0,他引:1
XuZhang En-minFeng 《应用数学学报(英文版)》2004,20(3):401-410
This paper studies the two-dimensional layout optimization problem.An optimization model withperformance constraints is presented.The layout problem is partitioned into finite subproblems in terms ofgraph theory,in such a way of that each subproblem overcomes its on-off nature optimal variable.A minimaxproblem is constructed that is locally equivalent to each subproblem.By using this minimax problem,we presentthe optimality function for every subproblem and prove that the first order necessary optimality condition issatisfied at a point if and only if this point is a zero of optimality function. 相似文献