全文获取类型
收费全文 | 3271篇 |
免费 | 253篇 |
国内免费 | 170篇 |
专业分类
电工技术 | 443篇 |
综合类 | 316篇 |
化学工业 | 77篇 |
金属工艺 | 113篇 |
机械仪表 | 342篇 |
建筑科学 | 95篇 |
矿业工程 | 80篇 |
能源动力 | 63篇 |
轻工业 | 125篇 |
水利工程 | 22篇 |
石油天然气 | 20篇 |
武器工业 | 10篇 |
无线电 | 209篇 |
一般工业技术 | 290篇 |
冶金工业 | 47篇 |
原子能技术 | 3篇 |
自动化技术 | 1439篇 |
出版年
2024年 | 4篇 |
2023年 | 17篇 |
2022年 | 42篇 |
2021年 | 42篇 |
2020年 | 46篇 |
2019年 | 48篇 |
2018年 | 78篇 |
2017年 | 81篇 |
2016年 | 91篇 |
2015年 | 125篇 |
2014年 | 184篇 |
2013年 | 195篇 |
2012年 | 218篇 |
2011年 | 237篇 |
2010年 | 170篇 |
2009年 | 228篇 |
2008年 | 218篇 |
2007年 | 283篇 |
2006年 | 243篇 |
2005年 | 216篇 |
2004年 | 151篇 |
2003年 | 126篇 |
2002年 | 95篇 |
2001年 | 72篇 |
2000年 | 59篇 |
1999年 | 42篇 |
1998年 | 50篇 |
1997年 | 37篇 |
1996年 | 40篇 |
1995年 | 39篇 |
1994年 | 38篇 |
1993年 | 16篇 |
1992年 | 24篇 |
1991年 | 15篇 |
1990年 | 13篇 |
1989年 | 17篇 |
1988年 | 14篇 |
1987年 | 9篇 |
1986年 | 7篇 |
1985年 | 8篇 |
1984年 | 8篇 |
1983年 | 8篇 |
1982年 | 10篇 |
1981年 | 3篇 |
1980年 | 4篇 |
1979年 | 4篇 |
1977年 | 4篇 |
1975年 | 2篇 |
1963年 | 2篇 |
1961年 | 3篇 |
排序方式: 共有3694条查询结果,搜索用时 15 毫秒
41.
Nonlinear model predictive control with relevance vector regression and particle swarm optimization 总被引:1,自引:0,他引:1
In this paper, a nonlinear model predictive control strategy which utilizes a probabilistic sparse kernel learning technique called relevance vector regression (RVR) and particle swarm optimization with controllable random exploration velocity (PSO-CREV) is applied to a catalytic continuous stirred tank reactor (CSTR) process. An accurate reliable nonlinear model is first identified by RVR with a radial basis function (RBF) kernel and then the optimization of control sequence is speeded up by PSO-CREV. Additional stochastic behavior in PSO-CREV is omitted for faster convergence of nonlinear optimization. An improved system performance is guaranteed by an accurate sparse predictive model and an efficient and fast optimization algorithm. To compare the performance, model predictive control (MPC) using a deterministic sparse kernel learning technique called Least squares support vector machines (LS-SVM) regression is done on a CSTR. Relevance vector regression shows improved tracking performance with very less computation time which is much essential for real time control. 相似文献
42.
迭代重加权最小二乘支持向量机快速算法研究 总被引:3,自引:0,他引:3
迭代重加权(Iteratively Reweighted)方法是提高最小二乘支持向量机(LS-SVM)稳健性的重要手段,但由于涉及到多次加权和重复训练,该方法需要大量运算,无法广泛应用.通过数值推导,获得了求解迭代重加权最小二乘支持向量机(IRLS-SVM)的快速算法,大幅度减少了其运算复杂度.引入了3种经典的加权函数,并在多个仿真数据集和实际数据集上进行实验,证实了IRLS-SVM能获得相当稳健的学习结果,所提出的快速算法也确实能够大幅度减少训练时间.实验结果同时表明,在快速训练算法的框架下,3种不同的权重函数可能要求不同的训练时间. 相似文献
43.
Karim O. Elish Author Vitae Author Vitae 《Journal of Systems and Software》2008,81(5):649-660
Effective prediction of defect-prone software modules can enable software developers to focus quality assurance activities and allocate effort and resources more efficiently. Support vector machines (SVM) have been successfully applied for solving both classification and regression problems in many applications. This paper evaluates the capability of SVM in predicting defect-prone software modules and compares its prediction performance against eight statistical and machine learning models in the context of four NASA datasets. The results indicate that the prediction performance of SVM is generally better than, or at least, is competitive against the compared models. 相似文献
44.
Feature selection is an important aspect of solving data-mining and machine-learning problems. This paper proposes a feature-selection
method for the Support Vector Machine (SVM) learning. Like most feature-selection methods, the proposed method ranks all features
in decreasing order of importance so that more relevant features can be identified. It uses a novel criterion based on the
probabilistic outputs of SVM. This criterion, termed Feature-based Sensitivity of Posterior Probabilities (FSPP), evaluates
the importance of a specific feature by computing the aggregate value, over the feature space, of the absolute difference
of the probabilistic outputs of SVM with and without the feature. The exact form of this criterion is not easily computable
and approximation is needed. Four approximations, FSPP1-FSPP4, are proposed for this purpose. The first two approximations
evaluate the criterion by randomly permuting the values of the feature among samples of the training data. They differ in
their choices of the mapping function from standard SVM output to its probabilistic output: FSPP1 uses a simple threshold
function while FSPP2 uses a sigmoid function. The second two directly approximate the criterion but differ in the smoothness
assumptions of criterion with respect to the features. The performance of these approximations, used in an overall feature-selection
scheme, is then evaluated on various artificial problems and real-world problems, including datasets from the recent Neural
Information Processing Systems (NIPS) feature selection competition. FSPP1-3 show good performance consistently with FSPP2
being the best overall by a slight margin. The performance of FSPP2 is competitive with some of the best performing feature-selection
methods in the literature on the datasets that we have tested. Its associated computations are modest and hence it is suitable
as a feature-selection method for SVM applications.
Editor: Risto Miikkulainen. 相似文献
45.
This paper addresses the problem of fault detection and isolation for a particular class of discrete event dynamical systems
called hierarchical finite state machines (HFSMs). A new version of the property of diagnosability for discrete event systems
tailored to HFSMs is introduced. This notion, called L1-diagnosability, captures the possibility of detecting an unobservable fault event using only high level observations of the
behavior of an HFSM. Algorithms for testing L1-diagnosability are presented. In addition, new methodologies are presented for studying the diagnosability properties of
HFSMs that are not L1-diagnosable. These methodologies avoid the complete expansion of an HFSM into its corresponding flat automaton by focusing
the expansion on problematic indeterminate cycles only in the associated extended diagnoser.
Andrea Paoli received the master degree in Computer Science Engineering and the Ph.D. in Automatic Control and Operational Research from the University of Bologna in 2000 and 2003 respectively. He currently holds a Post Doc position at the Department of Electronics, Computer Science and Systems (DEIS) at the University of Bologna, Italy. He is a member of the Center for Research on Complex Automated Systems (CASY) Giuseppe Evangelisti. From August to January 2002, and in March 2005 he held visiting positions at the Department of Electrical Engineering and Computer Science at The University of Michigan, Ann Arbor. In July 2005 he won the prize IFAC Outstanding AUTOMATICA application paper award for years 2002-2005 for the article by Claudio Bonivento, Alberto Isidori, Lorenzo Marconi, Andrea Paoli titled Implicit fault-tolerant control: application to induction motors appeared on AUTOMATICA issue 30(4). Since 2006 he is a member of the IFAC Technical Committee on Fault Detection, Supervision and Safety of Technical Processes (IFAC SAFEPROCESS TC). His current research interests focus on Fault Tolerant Control and Fault Diagnosis in distributed systems and in discrete event systems and on industrial automation software architectures following an agent based approach. His theoretical background includes also nonlinear control and output regulation using geometric approach. Stéphane Lafortune received the B. Eng degree from Ecole Polytechnique de Montréal in 1980, the M. Eng. degree from McGill University in 1982, and the Ph.D. degree from the University of California at Berkeley in 1986, all in electrical engineering. Since September 1986, he has been with the University of Michigan, Ann Arbor, where he is a Professor of Electrical Engineering and Computer Science. Dr. Lafortune is a Fellow of the IEEE (1999). He received the Presidential Young Investigator Award from the National Science Foundation in 1990 and the George S. Axelby Outstanding Paper Award from the Control Systems Society of the IEEE in 1994 (for a paper co-authored with S. L. Chung and F. Lin) and in 2001 (for a paper co-authored with G. Barrett). At the University of Michigan, he received the EECS Department Research Excellence Award in 1994–1995, the EECS Department Teaching Excellence Award in 1997–1998, and the EECS Outstanding Achievement Award in 2003–2004. Dr. Lafortune is a member of the editorial boards of the Journal of Discrete Event Dynamic Systems: Theory and Applications and of the International Journal of Control. His research interests are in discrete event systems modeling, diagnosis, control, and optimization. He is co-developer of the software packages DESUMA and UMDES. He co-authored, with C. Cassandras, the textbook Introduction to Discrete Event Systems—Second Edition (Springer, 2007). Recent publications and software tools are available at the Web site . 相似文献
Stéphane LafortuneEmail: |
Andrea Paoli received the master degree in Computer Science Engineering and the Ph.D. in Automatic Control and Operational Research from the University of Bologna in 2000 and 2003 respectively. He currently holds a Post Doc position at the Department of Electronics, Computer Science and Systems (DEIS) at the University of Bologna, Italy. He is a member of the Center for Research on Complex Automated Systems (CASY) Giuseppe Evangelisti. From August to January 2002, and in March 2005 he held visiting positions at the Department of Electrical Engineering and Computer Science at The University of Michigan, Ann Arbor. In July 2005 he won the prize IFAC Outstanding AUTOMATICA application paper award for years 2002-2005 for the article by Claudio Bonivento, Alberto Isidori, Lorenzo Marconi, Andrea Paoli titled Implicit fault-tolerant control: application to induction motors appeared on AUTOMATICA issue 30(4). Since 2006 he is a member of the IFAC Technical Committee on Fault Detection, Supervision and Safety of Technical Processes (IFAC SAFEPROCESS TC). His current research interests focus on Fault Tolerant Control and Fault Diagnosis in distributed systems and in discrete event systems and on industrial automation software architectures following an agent based approach. His theoretical background includes also nonlinear control and output regulation using geometric approach. Stéphane Lafortune received the B. Eng degree from Ecole Polytechnique de Montréal in 1980, the M. Eng. degree from McGill University in 1982, and the Ph.D. degree from the University of California at Berkeley in 1986, all in electrical engineering. Since September 1986, he has been with the University of Michigan, Ann Arbor, where he is a Professor of Electrical Engineering and Computer Science. Dr. Lafortune is a Fellow of the IEEE (1999). He received the Presidential Young Investigator Award from the National Science Foundation in 1990 and the George S. Axelby Outstanding Paper Award from the Control Systems Society of the IEEE in 1994 (for a paper co-authored with S. L. Chung and F. Lin) and in 2001 (for a paper co-authored with G. Barrett). At the University of Michigan, he received the EECS Department Research Excellence Award in 1994–1995, the EECS Department Teaching Excellence Award in 1997–1998, and the EECS Outstanding Achievement Award in 2003–2004. Dr. Lafortune is a member of the editorial boards of the Journal of Discrete Event Dynamic Systems: Theory and Applications and of the International Journal of Control. His research interests are in discrete event systems modeling, diagnosis, control, and optimization. He is co-developer of the software packages DESUMA and UMDES. He co-authored, with C. Cassandras, the textbook Introduction to Discrete Event Systems—Second Edition (Springer, 2007). Recent publications and software tools are available at the Web site . 相似文献
46.
Conventional clinical decision support systems are generally based on a single classifier or a simple combination of these models, showing moderate performance. In this paper, we propose a classifier ensemble-based method for supporting the diagnosis of cardiovascular disease (CVD) based on aptamer chips. This AptaCDSS-E system overcomes conventional performance limitations by utilizing ensembles of different classifiers. Recent surveys show that CVD is one of the leading causes of death and that significant life savings can be achieved if precise diagnosis can be made. For CVD diagnosis, our system combines a set of four different classifiers with ensembles. Support vector machines and neural networks are adopted as base classifiers. Decision trees and Bayesian networks are also adopted to augment the system. Four aptamer-based biochip data sets including CVD data containing 66 samples were used to train and test the system. Three other supplementary data sets are used to alleviate data insufficiency. We investigated the effectiveness of the ensemble-based system with several different aggregation approaches by comparing the results with single classifier-based models. The prediction performance of the AptaCDSS-E system was assessed with a cross-validation test. The experimental results show that our system achieves high diagnosis accuracy (>94%) and comparably small prediction difference intervals (<6%), proving its usefulness in the clinical decision process of disease diagnosis. Additionally, 10 possible biomarkers are found for further investigation. 相似文献
47.
在深入分析五轴数控系统的运动机构配置的基础上,针对传统的基于矩阵或欧拉角的插补算法在旋转空间难以解决线性插值和加工要求等问题, 设计一种基于四元数五轴联动的插补算法,不仅简化了插补计算量,同时能够使刀具从一点平稳的运动到另一点,而且插补的轨迹更光滑连续.文章引入四元数理论,重点研究了四元数在构造数学模型和运动变换中的应用,并在Matlab中成功的进行了仿真.实验结果表明了该算法的可行性和可靠性. 相似文献
48.
提出了一种基于支持向量机的改进的降维方法.在输入和特征空间中,特征子集的选取分别根据原始特征每一维对分类的贡献来获得.最后,通过将输入和特征空间中的特征选取联合起来,得到了一种改进的降维方法.实验表明:使用这种方法,在保持对分类准确率不受明显的影响的同时,能大大地提高训练和预测的速度. 相似文献
49.
基于支持向量机的参数自整定PID非线性系统控制 总被引:3,自引:0,他引:3
对非线性系统提出了一种基于支持向量机的自整定PID控制新方法.用支持向量机辨识系统的非线性关系,并对之进行线性化,提取出瞬时线性模型,采用最小方差的准则获取PID控制器的最优参数.为改善控制器的性能,提出了一些改进措施,包括使用一阶滤波器、控制器参数更新标准及惩罚系数的调整等.通过对典型非线性系统的仿真,验证了该方法的有效性和可行性. 相似文献
50.