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1.
Predicting consumption behavior is very important for adjusting supplier production plans and enterprise marketing activities. Conventional statistical methods are unable to accurately predict green consumption behavior because it is characterized by multivariate nonlinear interactions. The paper proposes an optimized fruit fly algorithm (FOA) and extreme learning machine (ELM) model for consumption behavior prediction. First, to address the problem of uneven search direction of FOA leading to insufficient search ability and low efficiency, the paper proposes a sector search mechanism instead of a random search mechanism to improve the global search ability and convergence speed of FOA. Second, to address the issue that the initial weights and hidden layer bias values of the ELM are randomly generated, which affects the learning efficiency and generalization of the ELM, the paper uses an improved FOA to optimize the weights and bias values of ELM for improving the prediction accuracy. Taking the green vegetable consumption behavior of Beijing residents as an example, the results show the optimization of the initial weight and threshold of ELM by the GA, PSO, FOA, and SFOA, the prediction accuracy of the GA-ELM, PSO-ELM, FOA-ELM, and SFOA-ELM models all surpass those of ELM. Compared with BPNN, GRNN, ELM, GA-ELM, PSO-ELM, and FOA-ELM models, the RMSE value of SFOA-ELM was decreased by 9.45%, 8.40%, 11.89%, 5.84%, 2.22%, and 2.69%, respectively. These findings demonstrate the effectiveness of the SFOA-ELM model in green consumption behavior prediction and provide new ideas for the accurate prediction of consumption behaviors of other green products with similar characteristics. 相似文献
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随着电力变压器朝着大容量、高电压、高负荷的方向发展,其结构也越发复杂,故障逐渐增加.当前广泛使用的改良三比值法在对电力变压器进行故障诊断的过程中,容易出现准确率低和漏判率高等问题.鉴于此,提出一种NBC模型,该模型将朴素贝叶斯网络和改良三比值法的优势结合,在对电力变压器当前状态数据和历史样本数据充分学习的基础上,利用朴素贝叶斯分类器对当前电力变压器故障的编码组合进行故障预测分类,将预测所得到的诊断结果进行排序整理输出,实现了对电力变压器各类故障的有效分类.仿真结果表明,该NBC模型能较好地适应电力变压器各类故障的诊断,故障综合诊断正确率为95%,相较于改良三比值法,诊断准确率提升了50%,具有较强的工程实用性. 相似文献
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《International Journal of Forecasting》2021,37(4):1399-1425
Dynamic factor models have been the main “big data” tool used by empirical macroeconomists during the last 30 years. In this context, Kalman filter and smoothing (KFS) procedures can cope with missing data, mixed frequency data, time-varying parameters, non-linearities, non-stationarity, and many other characteristics often observed in real systems of economic variables. The main contribution of this paper is to provide a comprehensive updated summary of the literature on latent common factors extracted using KFS procedures in the context of dynamic factor models, pointing out their potential limitations. Signal extraction and parameter estimation issues are separately analyzed. Identification issues are also tackled in both stationary and non-stationary models. Finally, empirical applications are surveyed in both cases. This survey is relevant to researchers and practitioners interested not only in the theory of KFS procedures for factor extraction in dynamic factor models but also in their empirical application in macroeconomics and finance. 相似文献
5.
维护方式选择是维护管理中的重要工作之一,合理的维护方式既能达到保障设备的稳定运行,又能同时兼顾其它各个方面的要求。由于对维护方式的评价涉及多个部门、人员和属性,有些指标只是一个模糊的概念,因而采用模糊多属性群决策的方法对维护方式进行优先抉择。本文结合A公司的设备维护方式选择问题,尝试使用模糊多属性群决策折衷算法求解最佳的维护方式。 相似文献
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《Socio》2019
We study a location-allocation-routing problem for distribution of the injured in a disaster response scenario, considering a three-type transportation network with separate links. A circle-based approach to estimate the impacts of the disaster is presented. After formulating relations for computing the percentage of the injured, the destruction percentage and the damage-dependent travel times, the problem is formulated as an integer nonlinear program. We utilize a genetic algorithm and a discrete version of the imperialist competitive algorithm for solving large problems. An empirical study focused on earthquakes in Tabriz, Iran, illustrates applicability of the proposed model and performance of the proposed algorithms. 相似文献
7.
针对脉冲噪声下恒模算法(Constant Modulus Algorithm,CMA)失败的问题,通过分析脉冲噪声的影响,提出了一种基于最小均方(Least Mean Square,LMS)准则的对数型恒模算法(Logarithmic-type CMA,LT-CMA)。LT-CMA利用对数函数的非线性变换特性自适应地抑制强脉冲噪声对误差函数的影响,并且利用l2-范数进行信号归一化处理以增强算法的稳健性。仿真结果表明,所提出的LT-CMA可以适应于高斯噪声环境和脉冲噪声环境;与经典自适应均衡算法相比,在收敛速度和稳健性两方面上,所提出的LT-CMA都有显著的提升。 相似文献
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We first employ β-conditional convergence and log t regression tests based on nonlinear time-varying factor model and club clustering algorithm to analyze the convergence characteristics of the development level of Internet finance in 335 prefecture-level cities in China. The result of log t regression test illustrates that there is no convergence as a whole in the development level of China's Internet finance. However, seven convergence clubs and a divergent group have been formed, and the development level and growth rate of Internet finance among these convergence clubs have shown obvious differences. Moreover, we also employ the Ordered Probit to explore the formation mechanism of the convergence clubs. The results reveal that the regions with a higher level of economic development, traditional financial development, economic openness and Internet development are more inclined to converge in a club with a higher Internet finance development level. Alternatively, the regions that are interfered with more by the government or that have a lower degree of marketization, tend to converge in a club with a lower level. Finally, according to the conclusions, we propose corresponding policy recommendations for promoting the regional coordinated development of China's Internet finance. 相似文献
10.
The endo–exo problem lies at the heart of statistical identification in many fields of science, and is often plagued by spurious strong-and-long memory due to improper treatment of trends, shocks and shifts in the data. A class of models that has shown to be useful in discerning exogenous and endogenous activity is the Hawkes process. This class of point processes has enjoyed great recent popularity and rapid development within the quantitative finance literature, with particular focus on the study of market microstructure and high frequency price fluctuations. We show that there are important lessons from older fields like time series and econometrics that should also be applied in financial point process modelling. In particular, we emphasize the importance of appropriately treating trends and shocks for the identification of the strength and length of memory in the system. We exploit the powerful Expectation Maximization algorithm and objective statistical criteria (BIC) to select the flexibility of the deterministic background intensity. With these methods, we strongly reject the hypothesis that the considered financial markets are critical at univariate and bivariate microstructural levels. 相似文献