首页 | 本学科首页   官方微博 | 高级检索  
  按 检索   检索词:      
出版年份:   被引次数:   他引次数: 提示:输入*表示无穷大
  收费全文   235篇
  国内免费   21篇
  完全免费   15篇
  自动化技术   271篇
  2018年   2篇
  2016年   5篇
  2015年   3篇
  2014年   4篇
  2013年   8篇
  2012年   11篇
  2011年   28篇
  2010年   16篇
  2009年   28篇
  2008年   23篇
  2007年   31篇
  2006年   13篇
  2005年   22篇
  2004年   10篇
  2003年   12篇
  2002年   15篇
  2001年   17篇
  2000年   11篇
  1999年   2篇
  1998年   4篇
  1997年   2篇
  1996年   4篇
排序方式: 共有271条查询结果,搜索用时 156 毫秒
The complexity of semiconductor manufacturing is increasing due to the smaller feature sizes, greater number of layers, and existing process reentry characteristics. As a result, it is difficult to manage and clarify responsibility for low yields in specific products. This paper presents a comprehensive data mining method for predicting and classifying the product yields in semiconductor manufacturing processes. A genetic programming (GP) approach, capable of constructing a yield prediction system and performing automatic discovery of the significant factors that might cause low yield, is presented. Comparison with the results then is performed using a decision tree induction algorithm. Moreover, this research illustrates the robustness and effectiveness of this method using a well-known DRAM fab’s real data set, with discussion of the results. Received: November 2004 / Accepted: September 2005  相似文献
Multi-objective optimization has played a major role in solving problems where two or more conflicting objectives need to be simultaneously optimized. This paper presents a Multi-Objective grammar-based genetic programming (MOGGP) system that automatically evolves complete rule induction algorithms, which in turn produce both accurate and compact rule models. The system was compared with a single objective GGP and three other rule induction algorithms. In total, 20 UCI data sets were used to generate and test generic rule induction algorithms, which can be now applied to any classification data set. Experiments showed that, in general, the proposed MOGGP finds rule induction algorithms with competitive predictive accuracies and more compact models than the algorithms it was compared with.
Gisele L. PappaEmail: Email:
遗传算法综述   总被引:3,自引:3,他引:163  
遗传算法来源于进化论和群体遗传学,是计算智能的重要组成部分,正受到众多学科的高度重视。本文系统综述了遗传算法的发展历程,理论研究和应用研究,并进行了分析和评价。  相似文献
遗传规划算法在化合物设计、筛选研究中的应用   总被引:2,自引:2,他引:0  
采用计算机科学中新兴的遗传规划算法思想,结合化学物质的本质特点,运用进化操作来实现化合物的合成设计和筛选。文中针对算法运用讨论了函数集、终止集问题,通过计算元素组成的字符串的化合价的结果来确定适应度函数,既符合化学学科的本质规律,又满足了算法的要求。通过复制、交换和突变操作,经过多代次的进化终止,取得了满意的结果。从实验可以看到,经过遗传规划算法的操作,得到了很多合理的化合物设计和筛选结果。所以说,该方法的应用有效地促进了化合物实验合成和筛选。文章还针对其实用性,从化学本质出发,提出了建议和研究方向。可以说本文是遗传规划在化学化合物合成筛选中运用的成功探索,同时也为进一步研究打下了坚实的基础。  相似文献
基于遗传编程的非线性系统辨识   总被引:1,自引:1,他引:0  
为了实现对非线性系统的辨识,能够对目标系统的结构和参数进行同步辨识,将遗传编程(Genetic Programming,GP)作为辨识工具.使用基本遗传编程算法对非线性静态系统进行辨识-对电厂钢球磨煤机存煤量与产粉量之间的特性关系曲线进行辨识;使用一种改进的遗传编程算法对非线性动态系统进行辨识-对一个二阶离散非线性差分方程进行辨识.所有辨识都取得了满意的结果.遗传编程进化过程中,目标系统的结构与参数同时准确辨识,证明遗传编程非常适合于解决非线性系统辨识问题,并在算法上实现了结构辨识和参数辨识的统一.  相似文献
自动程序修复方法研究进展   总被引:1,自引:1,他引:0  
自动程序修复帮助开发者降低人工修复bug的成本.基于测试集的修复方法旨在生成能够通过测试集的代码补丁,以使程序正常运行.回顾了基于测试集的程序修复的现有文献,按照自动修复方法和实证基础两个方面陈述了研究进展.首先,将已有的自动修复方法划分为3类,分别是基于搜索的、基于代码穷举的和基于约束求解的补丁生成方法;其次,细致地描述了程序修复的实证研究基础以及该研究领域中的争议;然后,简要介绍了程序修复的相关技术作为修复方法的补充;最后做出总结,描述了面临的机遇和挑战.  相似文献
Although computers have been applied in many areas, there are some applications which seem to be more difficult than others to computerise. Typically these are problems for which we do not have a complete understanding, such as computer vision or robot path planning. Traditional development methods cannot account for a poor analysis of a problem and therefore fail to deliver successful systems for ill-defined problems. Three case studies are presented to demonstrate the application of genetic algorithms and genetic programming to demonstrate how these evolutionary techniques can be applied to ill-defined problems, thus diminishing the need for humans to apply themselves to dangerous or mundane tasks.  相似文献
Genetic programming approach to determining of metal materials properties   总被引:1,自引:1,他引:0  
The paper deals with determining metal material properties by the use of genetic programming (GP). As an example, the determination of the flow stress in bulk forming is presented. The flow stress can be calculated on the basis of known forming efficiency. The experimental data obtained during pressure test serve as an environment to which models for forming efficiency have to be adapted during simulated evolution as much as possible. By performing four experiments, several different models for forming efficiency are genetically developed. The models are not a result of the human intelligence but of intelligent evolutionary process. With regard to their precision, the successful models are more or less equivalent; they differ mainly in size, shape, and complexity of solutions. The influence of selection of different initial model components (genes) on the probability of successful solution is studied in detail. In one especially successful run of the GP system the Siebel's expression was genetically developed. In addition, redundancy of the knowledge hidden in the experimental data was detected and eliminated without the influence of human intelligence. Researches showed excellent agreement between the experimental data, existing analytical solutions, and models obtained genetically.  相似文献
混合GP-GA用于信息系统建模预测的研究   总被引:1,自引:1,他引:10  
该文克服了传统建模方法在模型选取及参数估计方面的困难与不足,提出了利用改进的遗传程序设计和改进的遗传算法相结合的混合GP-GA算法。一方面,遗传程序设计中加入了简约压力项,控制了代码过度增长,实现了不加先验知识的简洁非线性模型的自动获取。另一方面,遗传算法采用Gray编码,随机整群抽样选择,以优化模型中的参数,这在一定程度上补偿了遗传程序设计在演化过程中具有较好结构的模型可能因为其中的参数未能达到最优而被淘汰的损失。仿真实例和实际应用均表明混合GP-GA算法优于普通的回归分析及单纯的遗传程序设计方法,提高了拟合和预测精度,并且更适合反映问题的实际情况。  相似文献
This work is concerned with the identification of models for nonlinear dynamical systems using multiobjective evolutionary algorithms. Systems modelling involves the processes of structure selection, parameter estimation, model performance and model validation and involves a complex solution space. Evolutionary Algorithms (EAs) are search and optimisation tools founded on the principles of natural evolution and genetics, which are suitable for a wide range of application areas. Due to the versatility of these tools and motivated by the versatility of genetic programming (GP), this evolutionary paradigm is proposed for this modelling problem. GP is then combined with a multiobjective function definition scheme. Multiobjective genetic programming (MOGP) is applied to multiple, conflicting objectives and yields a set of candidate parsimonious and valid models, which reproduce the original system behaviour. The MOGP approach is then demonstrated as being applicable for system modelling with chaotic dynamics. The circuit introduced by Chua, being one of the most popular benchmarks for studying nonlinear oscillations, and the Duffing–Holmes oscillator are the systems to test the evolutionary-based modelling approach introduced in this paper.  相似文献
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号