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81.
In real-world applications of optimization, optimal solutions are often of limited value, because disturbances of or changes to input data may diminish the quality of an optimal solution or even render it infeasible. One way to deal with uncertain input data is robust optimization, the aim of which is to find solutions which remain feasible and of good quality for all possible scenarios, i.e., realizations of the uncertain data. For single objective optimization, several definitions of robustness have been thoroughly analyzed and robust optimization methods have been developed. In this paper, we extend the concept of minmax robustness (Ben-Tal, Ghaoui, & Nemirovski, 2009) to multi-objective optimization and call this extension robust efficiency for uncertain multi-objective optimization problems. We use ingredients from robust (single objective) and (deterministic) multi-objective optimization to gain insight into the new area of robust multi-objective optimization. We analyze the new concept and discuss how robust solutions of multi-objective optimization problems may be computed. To this end, we use techniques from both robust (single objective) and (deterministic) multi-objective optimization. The new concepts are illustrated with some linear and quadratic programming instances. 相似文献
82.
The analytic hierarchy process with stochastic judgements 总被引:1,自引:0,他引:1
The analytic hierarchy process (AHP) is a widely-used method for multicriteria decision support based on the hierarchical decomposition of objectives, evaluation of preferences through pairwise comparisons, and a subsequent aggregation into global evaluations. The current paper integrates the AHP with stochastic multicriteria acceptability analysis (SMAA), an inverse-preference method, to allow the pairwise comparisons to be uncertain. A simulation experiment is used to assess how the consistency of judgements and the ability of the SMAA-AHP model to discern the best alternative deteriorates as uncertainty increases. Across a range of simulated problems results indicate that, according to conventional benchmarks, judgements are likely to remain consistent unless uncertainty is severe, but that the presence of uncertainty in almost any degree is sufficient to make the choice of best alternative unclear. 相似文献
83.
《Applied Mathematical Modelling》2014,38(15-16):3987-4005
In this study, we reduce the uncertainty embedded in secondary possibility distribution of a type-2 fuzzy variable by fuzzy integral, and apply the proposed reduction method to p-hub center problem, which is a nonlinear optimization problem due to the existence of integer decision variables. In order to optimize p-hub center problem, this paper develops a robust optimization method to describe travel times by employing parametric possibility distributions. We first derive the parametric possibility distributions of reduced fuzzy variables. After that, we apply the reduction methods to p-hub center problem and develop a new generalized value-at-risk (VaR) p-hub center problem, in which the travel times are characterized by parametric possibility distributions. Under mild assumptions, we turn the original fuzzy p-hub center problem into its equivalent parametric mixed-integer programming problems. So, we can solve the equivalent parametric mixed-integer programming problems by general-purpose optimization software. Finally, some numerical experiments are performed to demonstrate the new modeling idea and the efficiency of the proposed solution methods. 相似文献
84.
《International Journal of Approximate Reasoning》2014,55(9):2049-2070
The behaviour of norm-autonomous agents is determined by their goals and the norms that are explicitly represented inside their minds. Thus, they require mechanisms for acquiring and accepting norms, determining when norms are relevant to their case, and making decisions about norm compliance. Up until now the existing proposals on norm-autonomous agents assume that agents interact within a deterministic environment that is certainly perceived. In practise, agents interact by means of sensors and actuators under uncertainty with non-deterministic and dynamic environments. Therefore, the existing proposals are unsuitable or, even, useless to be applied when agents have a physical presence in some real-world environment. In response to this problem we have developed the n-BDI architecture. In this paper, we propose a multi-context graded BDI architecture (called n-BDI) that models norm-autonomous agents able to deal with uncertainty in dynamic environments. The n-BDI architecture has been experimentally evaluated and the results are shown in this paper. 相似文献
85.
In this paper we model concession contracts between a public and a private party, under dynamic uncertainty arising both from the volatility of the cash flow generated by the project and by the strategic behaviour of the two parties. Under these conditions we derive three notions of equilibrium price and apply the model to a case study for one of the most important concession contracts in Italy. 相似文献
86.
Michael Thompson 《Trends in analytical chemistry : TRAC》2011,30(7):1168
Analytical chemists can advantageously use an uncertainty function to describe the performance of an analytical system in terms of the standard uncertainty or standard deviation as a function of the concentration of the analyte. This “characteristic function” is useful for estimating uncertainty at a new concentration. A similar function can be used to prescribe the uncertainty that is regarded as fit for purpose for a particular application. This “fitness function” is useful in setting standards of accuracy in proficiency tests and similar exercises without revealing the concentration of the analyte. In combination, these two functions provide a rational basis for method selection. 相似文献
87.
Yury Nikulin 《Journal of Heuristics》2008,14(4):391-402
This paper addresses the robust spanning tree problem with interval data, i.e. the case of classical minimum spanning tree
problem when edge weights are not fixed but take their values from some intervals associated with edges. The problem consists
of finding a spanning tree that minimizes so-called robust deviation, i.e. deviation from an optimal solution under the worst
case realization of interval weights. As it was proven in Kouvelis and Yu (Robust Discrete Optimization and Its Applications,
Kluwer Academic, Norwell, 1997), the problem is NP-hard, therefore it is of great interest to tackle it with some metaheuristic approach, namely simulated
annealing, in order to calculate an approximate solution for large scale instances efficiently. We describe theoretical aspects
and present the results of computational experiments. To the best of our knowledge, this is the first attempt to develop a
metaheuristic approach for solving the robust spanning tree problem. 相似文献
88.
W. Bremser 《Accreditation and quality assurance》1998,3(10):398-402
The principles of uncertainty-based data evaluation are explained. Based on a brief review of recent publications on the
topic, examples for application to standard procedures in analytical chemistry – calibration, method validation, standard
addition etc., – are given. The performance and the advantages of uncertainty-based data evaluation are discussed.
Received: 4 July 1998 · Accepted: 6 July 1998 相似文献
89.
Van-Nam Huynh Yoshiteru Nakamori Mina Ryoke Tu-Bao Ho 《Fuzzy Optimization and Decision Making》2007,6(3):255-278
This paper discusses the issue of how to use fuzzy targets in the target-based model for decision making under uncertainty.
After introducing a target-based interpretation of the expected value on which it is shown that this model implicitly assumes
a neutral behavior on attitude about the target, we examine the issue of using fuzzy targets considering different attitudes
about the target selection of the decision maker. We also discuss the problem for situations on which the decision maker’s
attitude about target may change according to different states of nature. Especially, it is shown that the target-based approach
can provide an unified way for solving the problem of fuzzy decision making with uncertainty about the state of nature and
imprecision about payoffs. Several numerical examples are given for illustration of the discussed issues. 相似文献
90.
X.S. Qin G.H. Huang G.M. Zeng A. Chakma Y.F. Huang 《European Journal of Operational Research》2007,180(3):182
Planning for water quality management systems is complicated by a variety of uncertainties and nonlinearities, where difficulties in formulating and solving the resulting inexact nonlinear optimization problems exist. With the purpose of tackling such difficulties, this paper presents the development of an interval-fuzzy nonlinear programming (IFNP) model for water quality management under uncertainty. Methods of interval and fuzzy programming were integrated within a general framework to address uncertainties in the left- and right-hand sides of the nonlinear constraints. Uncertainties in water quality, pollutant loading, and the system objective were reflected through the developed IFNP model. The method of piecewise linearization was developed for dealing with the nonlinearity of the objective function. A case study for water quality management planning in the Changsha section of the Xiangjiang River was then conducted for demonstrating applicability of the developed IFNP model. The results demonstrated that the accuracy of solutions through linearized method normally rises positively with the increase of linearization levels. It was also indicated that the proposed linearization method was effective in dealing with IFNP problems; uncertainties can be communicated into optimization process and generate reliable solutions for decision variables and objectives; the decision alternatives can be obtained by adjusting different combinations of the decision variables within their solution intervals. It also suggested that the linearized method should be used under detailed error analysis in tackling IFNP problems. 相似文献