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31.
针对高端装备研制数据的保密性和重要性,提出一种基于生成对抗网络(generative adversarial networks, GAN)的高端装备研制数据脱敏方法。面对高端装备研制数据在数据挖掘和数据共享时可能面临泄露的风险,利用GAN进行数据脱敏。在随机生成的高斯数据集上进行实验,通过比较源数据和脱敏数据的统计特征,证明GAN的数据脱敏方法能够有效实现数据脱敏过程中所要求的数据安全性、数据有效性和成本可控性。最后,在Yeast数据集上进行验证, GAN输出的脱敏数据同样在现实世界数据集上表现出色,能够准确地预测Yeast的分类,为高端装备研制数据的管理和分析提供了一种新的思路。 相似文献
32.
The identification of nonlinear systems with multiple sampled rates is a difficult task.The motivation of our paper is to study the parameter estimation problem of Hammerstein systems with dead-zone characteristics by using the dual-rate sampled data.Firstly,the auxiliary model identification principle is used to estimate the unmeasurable variables,and the recursive estimation algorithm is proposed to identify the parameters of the static nonlinear model with the dead-zone function and the parameters of the dynamic linear system model.Then,the convergence of the proposed identification algorithm is analyzed by using the martingale convergence theorem.It is proved theoretically that the estimated parameters can converge to the real values under the condition of continuous excitation.Finally,the validity of the proposed algorithm is proved by the identification of the dual-rate sampled nonlinear systems. 相似文献
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This work proposes a new approach for the prediction of the electricity price based on forecasting aggregated purchase and sale curves. The basic idea is to model the hourly purchase and the sale curves, to predict them and to find the intersection of the predicted curves in order to obtain the predicted equilibrium market price and volume. Modeling and forecasting of purchase and sale curves is performed by means of functional data analysis methods. More specifically, parametric (FAR) and nonparametric (NPFAR) functional autoregressive models are considered and compared to some benchmarks. An appealing feature of the functional approach is that, unlike other methods, it provides insights into the sale and purchase mechanism connected with the price and demand formation process and can therefore be used for the optimization of bidding strategies. An application to the Italian electricity market (IPEX) is also provided, showing that NPFAR models lead to a statistically significant improvement in the forecasting accuracy. 相似文献
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We consider finite state-space non-homogeneous hidden Markov models for forecasting univariate time series. Given a set of predictors, the time series are modeled via predictive regressions with state-dependent coefficients and time-varying transition probabilities that depend on the predictors via a logistic/multinomial function. In a hidden Markov setting, inference for logistic regression coefficients becomes complicated and in some cases impossible due to convergence issues. In this paper, we aim to address this problem utilizing the recently proposed Pólya-Gamma latent variable scheme. Also, we allow for model uncertainty regarding the predictors that affect the series both linearly — in the mean — and non-linearly — in the transition matrix. Predictor selection and inference on the model parameters are based on an automatic Markov chain Monte Carlo scheme with reversible jump steps. Hence the proposed methodology can be used as a black box for predicting time series. Using simulation experiments, we illustrate the performance of our algorithm in various setups, in terms of mixing properties, model selection and predictive ability. An empirical study on realized volatility data shows that our methodology gives improved forecasts compared to benchmark models. 相似文献
37.
Online search data provide us with a new perspective for quantifying public concern about animal diseases, which can be regarded as a major external shock to price fluctuations. We propose a modeling framework for pork price forecasting that incorporates online search data with support vector regression model. This novel framework involves three main steps: that is, formulation of the animal diseases composite indexes (ADCIs) based on online search data; forecast with the original ADCIs; and forecast improvement with the decomposed ADCIs. Considering that there are some noises within the online search data, four decomposition techniques are introduced: that is, wavelet decomposition, empirical mode decomposition, ensemble empirical mode decomposition, and singular spectrum analysis. The experimental study confirms the superiority of the proposed framework, which improves both the level and directional prediction accuracy. With the SSA method, the noise within the online search data can be removed and the performance of the optimal model is further enhanced. Owing to the long-term effect of diseases outbreak on price volatility, these improvements are more prominent in the mid- and long-term forecast horizons. 相似文献
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李天骄 《重庆工商大学学报(自然科学版)》2020,37(6):39-47
针对分层情况下的疾病流行率问题,考虑了金标准存在时,在各层的敏感度和特异度不同时二项比例(疾病流行率)的齐性检验;提出了基于渐近检验过程的7种统计量和基于Bootstrap重抽样检验过程的4种统计量,并通过蒙特卡罗模拟研究来比较了各种检验的犯第一类错误的概率和检验功效;研究表明:score统计量、似然比统计量和4种基于bootstrap重抽样的检验统计量具有良好的统计性质,推荐用于实际中;最后,实际数据进一步验证了方法的有效性。 相似文献
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过程变量在代表产品或服务过程信息时并非完美,而使用模糊数可能是另一较好途径。文章进一步完善模糊累积和控制图,其中使用中心和扩展具有重抽样分布的模糊随机变量,并给出模拟例证。 相似文献