排序方式: 共有99条查询结果,搜索用时 31 毫秒
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从关联理论探求译者主体性的发挥 总被引:1,自引:0,他引:1
冯岩松 《广东交通职业技术学院学报》2008,7(4):95-98
关联理论的翻译观认为翻译的过程就是寻求最佳关联的过程。最佳关联性是译者力争达到的目标,也是翻译的标准。而最佳关联性又取决于处理努力和语境效果。由于译者的认知不可能等同于原作者及译文读者的认知.译者必须通过推理构成对原著的认知心理图式,再通过译文将自己形成的认知图式传递给译文读者.努力使译文读者的期盼和原作意图相吻合,这就为译者提供了发挥主体能动性的空间。译者的主体性贯穿翻译始终。翻译的成败有赖于译者主体性的发挥。 相似文献
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基于模糊推理的区域路网交通状态分析方法 总被引:2,自引:0,他引:2
为准确判别区域路网的交通状态,对区域路网交通流的宏观特性进行了详细分析,建立了路网交通状态评估指标体系。结合区域路网的拓扑结构与交通流特征,基于模糊推理技术提出了路网交通状态分析方法。以城市主干道路网为例,验证了路网交通状态分析方法的有效性。该方法可应用于在线交通状态分析和历史数据库交通运行特征的提取,为交通管理决策提供基础信息。 相似文献
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由于驾驶行为的不确定性,难以建立精确的车辆跟驰模型。针对这一问题,应用自适应模糊神经推理系统(ANFIS)建立跟驰模型,以跟随车与前车速度差及行车间距为输入量、跟随车的加速度为输出量,建立25条模糊推理规则,将模糊推理规则产生的数据作为车辆跟驰ANFIS模型的训练数据,并利用MATLAB编程对其进行训练。最后,设计了基于车载高精度GPS的跟驰试验,并结合试验数据分别对自适应模糊神经推理系统跟驰模型和传统跟驰模型进行仿真。结果表明,前者输出的跟驰车辆加速度值更接近于真实值。 相似文献
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H. J. Kim C. H. Bae S. H. Kim H. Y. Lee K. J. Park M. W. Suh 《International Journal of Automotive Technology》2009,10(1):123-129
Urban transit is a complex system that contains both electrical and mechanical entities; therefore, it is necessary to construct
a maintenance system for ensuring safety during high-speed driving. Expert systems are computer programs that use numerical
or non-numerical domain-specific knowledge to solve problems. This research aims to develop an expert system that diagnoses
the causes of failures quickly and displays measures to correct them. For the development of this expert system, the standardization
of a failure code classification and the creation of a Bill of Materials (BOM) were first performed. Through the analysis
of both failure history and maintenance manuals, a knowledge base has been constructed. Also, for retrieving the procedure
of failure diagnosis and repair linking with the knowledge base, we have built a Rule-Based Reasoning (RRB) engine with a
pattern matching technique and a Case-Based Reasoning (CBR) engine with a similar search method. Finally, this system has
been developed as web based in order to maximize accessibility. 相似文献
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Bayesian committee of neural networks to predict travel times with confidence intervals 总被引:1,自引:0,他引:1
C.P.IJ. van Hinsbergen J.W.C. van Lint H.J. van Zuylen 《Transportation Research Part C: Emerging Technologies》2009,17(5):498-509
Short-term prediction of travel time is one of the central topics in current transportation research and practice. Among the more successful travel time prediction approaches are neural networks and combined prediction models (a ‘committee’). However, both approaches have disadvantages. Usually many candidate neural networks are trained and the best performing one is selected. However, it is difficult and arbitrary to select the optimal network. In committee approaches a principled and mathematically sound framework to combine travel time predictions is lacking. This paper overcomes the drawbacks of both approaches by combining neural networks in a committee using Bayesian inference theory. An ‘evidence’ factor can be calculated for each model, which can be used as a stopping criterion during training, and as a tool to select and combine different neural networks. Along with higher prediction accuracy, this approach allows for accurate estimation of confidence intervals for the predictions. When comparing the committee predictions to single neural network predictions on the A12 motorway in the Netherlands it is concluded that the approach indeed leads to improved travel time prediction accuracy. 相似文献
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本文对不同进口方向的多辆应急车辆在同一时段内通过同一信号交叉口的协调控制问题进行研究,提出一种基于多智能体的多应急车辆信号优先控制系统.在该系统中引入了相位智能体和管理智能体,同时采用模糊推理理论实现各类智能体的内在逻辑,以及模块之间的协调机制.基于Starlogo多智能体仿真软件,针对特定十字交叉口的多应急车辆优先信号协调控制案例进行仿真实验.结果表明,多应急车辆信号优先控制策略能实现应急车辆在交叉口时间通行权上的优先,同时能够减少应急车辆对其他车辆的干扰,对于提高城市范围内的紧急救援效率,具有一定的实用价值. 相似文献