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31.
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
36.
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
40.
通过采用机器学习算法,提出了高温作用后砂岩物理力学性质变化的阈值温度的确定方法。基于Python语言,利用K-Means、SVM算法,实现了对样本数据的分类和聚类,确定了阈值温度区间,验证了二分类法的合理性和准确性.结果表明,砂岩样本的阈值温度区间为400~600 ℃,阈值温度上下砂岩的物理力学性质存在显著差异,体现为阈值温度区间以下的岩样物理力学性质较为分散,而阈值温度区间以上的岩样物理力学性质较为集中. 相似文献