Doxygen的词根来源于Document（文档）和Oxygen（氧气），它是一个功能强大、使用方便且支持各种操作系统和编程语言的代码文档生成系统。Doxygen由荷兰人Dimitri van Heesch.开发，并且在GNU公共许可证（GPL）下发布，目前已经成为各主要的Linux发行版的附带组件。众多重量级的软件项目（如KDE，Qt、ACE库等）都选用Doxygen作为其编档工具生成项目文档。 Doxygen最初在Linux下开发，但已经被移植到多个操作系统平台下，包括Unix的各发行版本、MS Windows和Mac OS。Doxygen目前最新的版本是1.3.6，支持的编程语言包括 C++，C，Java，IDL（CORBA和MS风格），对Objective-C， PHP， C#和D语言也有部分支持。
标签： Document Doxygen Oxygen 文档
上传时间： 2013-12-19
上传用户：woshini123456
/* This a simple genetic algorithm implementation where the */ /* evaluation function takes positive values only and the */ /* fitness of an individual is the same as the value of the */ /* Objective function
标签： implementation evaluation algorithm function
上传时间： 2016-01-18
上传用户：wkchong
This a simple genetic algorithm implementation where the evaluation function takes positive values only and the fitness of an individual is the same as the value of the Objective function
标签： implementation evaluation algorithm function
上传时间： 2016-11-24
上传用户：kelimu
有版权争议的内容和木马病毒代码 开发环境： 请选择 Visual C++ Visual Basic DOS Unix_Linux C++ Builder Java Windows_Unix Delphi C-C++ PHP-PERL PHP Perl Python HTML Asm Pascal Borland C++ 其他 多平台 C++ VFP SQL PDF TEXT WORD VBScript JavaScript ASP CSharp CHM FlashMX matlab PowerBuilder PPT LabView Flex MathCAD VBA PalmOS IDL LISP VHDL Objective-C(重要) 详细功能: 4545 请认真阅读您的文件包然后写出其具体功能(至少要20个字)。尽量不要让站长把时间都花费在为您修正说明上。压缩包解压时不能有密码。系统会自动删除debug
标签： Visual Windows_Unix Unix_Linux Builder
上传时间： 2013-12-08
上传用户：PresidentHuang
Java technology is both a programming language and a platform. The Java programming language originated as part of a research project to develop advanced software for a wide variety of network devices and embedded systems. The goal was to develop a small, reliable, portable, distributed, real-time operating platform. When the project started, C++ was the language of choice. But over time the difficulties encountered with C++ grew to the point where the problems could best be addressed by creating an entirely new language platform. Design and architecture decisions drew from a variety of languages such as Eiffel, SmallTalk, Objective C, and Cedar/Mesa. The result is a language platform that has proven ideal for developing secure, distributed, network-based end-user applications in environments ranging from network-embedded devices to the World-Wide Web and the desktop
标签： programming language Java technology
上传时间： 2014-01-03
上传用户：huangld
This code proposes genetic algorithm (GA) to optimize the point-to-point trajectory planning for a 3-link robot arm. The Objective function for the proposed GA is to minimizing traveling time and space, while not exceeding a maximum pre-defined torque, without collision with any obstacle in the robot workspace.
标签： point-to-point trajectory algorithm proposes
上传时间： 2013-12-21
上传用户：chenxichenyue
将源代码转换成html,支持多操作系统，支持多种编程语言:Ada95, ASP, Assembler, Basic, C, C#, C++, Cg, CLIPS, Fortran, Haskell, Java, Markup, Modula2, Objective C, Pascal, Perl, PHP, Python, Renderman, Ruby, SQL, Tcl
上传时间： 2013-12-28
上传用户：爺的气质
3.缺少文件 4.所选类别和开发环境不对 5.乱写说明或说明不够认真 6.压缩文件有密码 7.源码重复或已经存在 请不要上传有版权争议的内容和木马病毒代码 开发环境： 请选择 Visual C++ Visual Basic DOS Unix_Linux C++ Builder Java Windows_Unix Delphi C-C++ PHP-PERL PHP Perl Python HTML Asm Pascal Borland C++ Others MultiPlatform C++ VFP SQL PDF TEXT WORD VBScript JavaScript ASP CSharp CHM FlashMX matlab PowerBuilder PPT LabView Flex MathCAD VBA PalmOS IDL LISP VHDL Objective-C Fortran tcl/tk QT(重要) 功能描述: 请认真阅读您的文件包然后写出
上传时间： 2017-08-18
上传用户：stewart·
Computational models are commonly used in engineering design and scientific discovery activities for simulating complex physical systems in disciplines such as fluid mechanics, structural dynamics, heat transfer, nonlinear structural mechanics, shock physics, and many others. These simulators can be an enormous aid to engineers who want to develop an understanding and/or predictive capability for complex behaviors typically observed in the corresponding physical systems. Simulators often serve as virtual prototypes, where a set of predefined system parameters, such as size or location dimensions and material properties, are adjusted to improve the performance of a system, as defined by one or more system performance Objectives. Such optimization or tuning of the virtual prototype requires executing the simulator, evaluating performance Objective(s), and adjusting the system parameters in an iterative, automated, and directed way. System performance Objectives can be formulated, for example, to minimize weight, cost, or defects; to limit a critical temperature, stress, or vibration response; or to maximize performance, reliability, throughput, agility, or design robustness. In addition, one would often like to design computer experiments, run parameter studies, or perform uncertainty quantification (UQ). These approaches reveal how system performance changes as a design or uncertain input variable changes. Sampling methods are often used in uncertainty quantification to calculate a distribution on system performance measures, and to understand which uncertain inputs contribute most to the variance of the outputs. A primary goal for Dakota development is to provide engineers and other disciplinary scientists with a systematic and rapid means to obtain improved or optimal designs or understand sensitivity or uncertainty using simulationbased models. These capabilities generally lead to improved designs and system performance in earlier design stages, alleviating dependence on physical prototypes and testing, shortening design cycles, and reducing product development costs. In addition to providing this practical environment for answering system performance questions, the Dakota toolkit provides an extensible platform for the research and rapid prototyping of customized methods and meta-algorithms
标签： Optimization and Uncertainty Quantification
上传时间： 2016-04-08
上传用户：huhu123456
在微电网调度过程中综合考虑经济、环境、蓄电池的 循环电量，建立多目标优化数学模型。针对传统多目标粒子 群算法(multi-Objective particle swarm optimization，MOPSO) 的不足，提出引入模糊聚类分析的多目标粒子群算法 (multi-Objective particle swarm optimization algorithm based on fuzzy clustering，FCMOPSO)，在迭代过程中引入模糊聚 类分析来寻找每代的集群最优解。与 MOPSO 相比， FCMOPSO 增强了算法的稳定性与全局搜索能力，同时使优 化结果中 Pareto 前沿分布更均匀。在求得 Pareto 最优解集 后，再根据各目标的重要程度，用模糊模型识别从最优解集 中找出不同情况下的最优方案。最后以一欧洲典型微电网为 例，验证算法的有效性和可行性。
上传时间： 2019-11-11
上传用户：Dr.赵劲帅
