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31.
对股份制公司的综合投资方案的决策问题进行了研究.首先依据多个投资方案的风险与收益并存的实际情况,建立了最佳投资组合方案的多目标决策模型.然后,由董事会综合各股东所持股份和相互评价权值,利用群决策的方法得到一个最终投资方案,此方案在理论上能使公司获得最大收益. 相似文献
32.
On the Evaluation of Uncertain Courses of Action 总被引:3,自引:0,他引:3
Ronald R. Yager 《Fuzzy Optimization and Decision Making》2002,1(1):13-41
We consider the problem of decision making under uncertainty. The fuzzy measure is introduced as a general way of representing available information about the uncertainty. It is noted that generally in uncertain environments the problem of comparing alternative courses of action is difficult because of the multiplicity of possible outcomes for any action. One approach is to convert this multiplicity of possible of outcomes associated with an alternative into a single value using a valuation function. We describe various ways of providing a valuation function when the uncertainty is represented using a fuzzy measure. We then specialize these valuation functions to the cases of probabilistic and possibilistic uncertainty. 相似文献
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具有交易成本的证券组合投资决策研究 总被引:2,自引:0,他引:2
本文利用均值-方差模型,分析了有交易成本的证券投资组合的决策问题,给出了风险资产和无风险资产的最优投资比例与交易成本关系的一个有意义的结论。 相似文献
35.
多指标区间决策的理想点贴近法 总被引:5,自引:0,他引:5
研究了指标的权重不能完全确定但知道其所在区域的条件下的多指标决策问题 ,给出了方案与理想解的贴近度及其算法 ,按贴近度的大小可以对方案进行排序 .它同传统的决策方法相比较 ,具有需要信息量少 ,简单可靠等特点 .最后用该决策方法分析了一个实际问题 . 相似文献
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Alina Constantinescu 《Applications of Mathematics》2007,52(4):321-326
In one if his paper Luo transformed the problem of sum-fuzzy rationality into artificial learning procedure and gave an algorithm
which used the learning rule of perception. This paper extends the Luo method for finding a sum-fuzzy implementation of a
choice function and offers an algorithm based on the artificial learning procedure with fixed fraction. We also present a
concrete example which uses this algorithm. 相似文献
39.
群体决策的偏好协调性检验 总被引:2,自引:0,他引:2
对于一类群体决策问题,本文引进群体的偏好协调性指标,并且给出了偏好协调性指标的统计检验.在此基础上,还提出一个求该类群体决策问题的方法,以及讨论了群体的偏好快调性指标和群体决策结果间的关系. 相似文献
40.
Faison P. Gibson 《Computational & Mathematical Organization Theory》2007,13(1):39-61
An important aspect of learning is the ability to transfer knowledge to new contexts. However, in dynamic decision tasks, such as bargaining, firefighting, and process control, where decision makers must make repeated decisions under time pressure and outcome feedback may relate to any of a number of decisions, such transfer has proven elusive. This paper proposes a two-stage connectionist model which hypothesizes that decision makers learn to identify categories of evidence requiring similar decisions as they perform in dynamic environments. The model suggests conditions under which decision makers will be able to use this ability to help them in novel situations. These predictions are compared against those of a one-stage decision model that does not learn evidence categories, as is common in many current theories of repeated decision making. Both models' predictions are then tested against the performance of decision makers in an Internet bargaining task. Both models correctly predict aspects of decision makers' learning under different interventions. The two-stage model provides closer fits to decision maker performance in a new, related bargaining task and accounts for important features of higher-performing decision makers' learning. Although frequently omitted in recent accounts of repeated decision making, the processes of evidence category formation described by the two-stage model appear critical in understanding the extent to which decision makers learn from feedback in dynamic tasks.
Faison (Bud) Gibson is an Assistant Professor at College of Business, Eastern Michigan University. He has extensive experience developing and empirically testing models of decision behavior in dynamic decision environments. 相似文献