Doxygen的詞根來源于Document（文檔）和Oxygen（氧氣），它是一個功能強大、使用方便且支持各種操作系統和編程語言的代碼文檔生成系統。Doxygen由荷蘭人Dimitri van Heesch.開發，并且在GNU公共許可證（GPL）下發布，目前已經成為各主要的Linux發行版的附帶組件。眾多重量級的軟件項目（如KDE，Qt、ACE庫等）都選用Doxygen作為其編檔工具生成項目文檔。 Doxygen最初在Linux下開發，但已經被移植到多個操作系統平臺下，包括Unix的各發行版本、MS Windows和Mac OS。Doxygen目前最新的版本是1.3.6，支持的編程語言包括 C++，C，Java，IDL（CORBA和MS風格），對objective-C， PHP， C#和D語言也有部分支持。
標簽： Document Doxygen Oxygen 文檔
上傳時間： 2013-12-19
上傳用戶：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
標簽： implementation evaluation algorithm function
上傳時間： 2016-01-18
上傳用戶：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
標簽： implementation evaluation algorithm function
上傳時間： 2016-11-24
上傳用戶：kelimu
有版權爭議的內容和木馬病毒代碼 開發環境： 請選擇 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 多平臺 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) 詳細功能: 4545 請認真閱讀您的文件包然后寫出其具體功能(至少要20個字)。盡量不要讓站長把時間都花費在為您修正說明上。壓縮包解壓時不能有密碼。系統會自動刪除debug
標簽： Visual Windows_Unix Unix_Linux Builder
上傳時間： 2013-12-08
上傳用戶：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
標簽： programming language Java technology
上傳時間： 2014-01-03
上傳用戶：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.
標簽： point-to-point trajectory algorithm proposes
上傳時間： 2013-12-21
上傳用戶：chenxichenyue
將源代碼轉換成html,支持多操作系統，支持多種編程語言:Ada95, ASP, Assembler, Basic, C, C#, C++, Cg, CLIPS, Fortran, Haskell, Java, Markup, Modula2, objective C, Pascal, Perl, PHP, Python, Renderman, Ruby, SQL, Tcl
上傳時間： 2013-12-28
上傳用戶：爺的氣質
3.缺少文件 4.所選類別和開發環境不對 5.亂寫說明或說明不夠認真 6.壓縮文件有密碼 7.源碼重復或已經存在 請不要上傳有版權爭議的內容和木馬病毒代碼 開發環境： 請選擇 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: 請認真閱讀您的文件包然后寫出
上傳時間： 2017-08-18
上傳用戶：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
標簽： Optimization and Uncertainty Quantification
上傳時間： 2016-04-08
上傳用戶：huhu123456
在微電網調度過程中綜合考慮經濟、環境、蓄電池的 循環電量，建立多目標優化數學模型。針對傳統多目標粒子 群算法(multi-objective particle swarm optimization，MOPSO) 的不足，提出引入模糊聚類分析的多目標粒子群算法 (multi-objective particle swarm optimization algorithm based on fuzzy clustering，FCMOPSO)，在迭代過程中引入模糊聚 類分析來尋找每代的集群最優解。與 MOPSO 相比， FCMOPSO 增強了算法的穩定性與全局搜索能力，同時使優 化結果中 Pareto 前沿分布更均勻。在求得 Pareto 最優解集 后，再根據各目標的重要程度，用模糊模型識別從最優解集 中找出不同情況下的最優方案。最后以一歐洲典型微電網為 例，驗證算法的有效性和可行性。
上傳時間： 2019-11-11
上傳用戶：Dr.趙勁帥
