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11.
We develop two implementable algorithms, the first for the solution of finite and the second for the solution of semi-infinite min-max-min problems. A smoothing technique (together with discretization for the semi-infinite case) is used to construct a sequence of approximating finite min-max problems, which are solved with increasing precision. The smoothing and discretization approximations are initially coarse, but are made progressively finer as the number of iterations is increased. This reduces the potential ill-conditioning due to high smoothing precision parameter values and computational cost due to high levels of discretization. The behavior of the algorithms is illustrated with three semi-infinite numerical examples. 相似文献
12.
In this paper we consider the problem of estimating an unknown joint distribution which is defined over mixed discrete and continuous variables. A nonparametric kernel approach is proposed with smoothing parameters obtained from the cross-validated minimization of the estimator's integrated squared error. We derive the rate of convergence of the cross-validated smoothing parameters to their ‘benchmark’ optimal values, and we also establish the asymptotic normality of the resulting nonparametric kernel density estimator. Monte Carlo simulations illustrate that the proposed estimator performs substantially better than the conventional nonparametric frequency estimator in a range of settings. The simulations also demonstrate that the proposed approach does not suffer from known limitations of the likelihood cross-validation method which breaks down with commonly used kernels when the continuous variables are drawn from fat-tailed distributions. An empirical application demonstrates that the proposed method can yield superior predictions relative to commonly used parametric models. 相似文献
13.
14.
卡尔曼滤波法用于ICP-AES分析时,在白色噪音情况下能给出准确的分析结果,当试样的某些组分未知时,测量噪音将不是白色的,分析结果将不准确。本文提出了一种加权增量卡尔曼滤波法来解决这个问题。 相似文献
15.
Klaus Ziegler 《Journal of multivariate analysis》1997,62(2):233-272
Functional central limit theorems for triangular arrays of rowwise independent stochastic processes are established by a method replacing tail probabilities by expectations throughout. The main tool is a maximal inequality based on a preliminary version proved by P. Gaenssler and Th. Schlumprecht. Its essential refinement used here is achieved by an additional inequality due to M. Ledoux and M. Talagrand. The entropy condition emerging in our theorems was introduced by K. S. Alexander, whose functional central limit theorem for so-calledmeasure-like processeswill be also regained. Applications concern, in particular, so-calledrandom measure processeswhich include function-indexed empirical processes and partial-sum processes (with random or fixed locations). In this context, we obtain generalizations of results due to K. S. Alexander, M. A. Arcones, P. Gaenssler, and K. Ziegler. Further examples include nonparametric regression and intensity estimation for spatial Poisson processes. 相似文献
16.
17.
卡尔曼滤波紫外光度法同时测定饮料中苯甲酸及糖精的研究 总被引:8,自引:0,他引:8
在酸性条件下,用乙醚萃取、蒸干乙醚后,用NaHCO3溶解残渣,从复杂的样品中分离出苯甲酸和糖精,测定它们在245~281nm之间的紫外吸收,运用卡尔曼滤波算法计算两者的浓度,用新息序列监测干扰物质分离的程度。此法简化了许多分离手续,用于饮料分析,结果满意。 相似文献
18.
19.
The computation ofL
1 smoothing splines on large data sets is often desirable, but computationally infeasible. A locally weighted, LAD smoothing spline based smoother is suggested, and preliminary results will be discussed. Specifically, one can seek smoothing splines in the spacesW
m
(D), with [0, 1]
n
D. We assume data of the formy
i
=f(t
i
)+
i
,i=1,..., N with {t
i
}
i=1
N
D, the
i
are errors withE(
i
)=0, andf is assumed to be inW
m
. An LAD smoothing spline is the solution,s
, of the following optimization problem
相似文献
20.
Mark A. Gallagher Kenneth W. Bauer Jr. Peter S. Maybeck 《Annals of Operations Research》1994,53(1):419-441
Data truncation is a commonly accepted method of dealing with initialization bias in discrete-event simulation. An algorithm for determining the appropriate initial-data truncation point for multivariate output is proposed. The technique entails averaging across independent replications and estimating a steady-state output model in a state-space framework. A Bayesian technique called Multiple Model Adaptive Estimation (MMAE) is applied to compute a time varying estimate of the output's steady-state mean vector. This MMAE implementation features the use, in paralle, of a bank of Kalman filters. Each filter is constructed under a different assumption concerning the output's steady-state mean vector. One of the filters assumes that the steady-state mean vector is accurately reflected by an estimate, called the assumed steady-state mean vector, taken from the last half of the simulation data. As the filters process the output through the effective transient, this particular filter becomes more likely (in a Bayesian sense) to be the best filter to represent the data and the MMAE mean estimator is influenced increasingly towards the assumed steady-state mean vector. The estimated truncation point is selected when a norm of the MMAE mean vector estimate is within a small tolerance of the assumed steady-state mean vector. A Monte Carlo analysis using data from simulations of open and closed queueing models is used to evaluate the technique. The evaluation criteria include the ability to construct accurate and reliable confidence regions for the mean response vector based on the truncated sequences. 相似文献
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