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说明：  dataset-dimensionality-reduction-python-master

布谷鸟优化算法是对参数进行优化的算法，为元启发式算法的一种(The optimization algorithm of cuckoo is an optimization algorithm for the parameters)

说明：  fluent动网格使用6DOF实现纸飞机的被动运动。(The Fluent dynamic grid uses 6DOF to realize the passive motion of paper aircraft.)

说明：  VC操作ADO和datagrid控件的源码，是动态的的。不受目录改变的限制(ADO and VC operation datagrid source control is the dynamic. From the directory to change the restrictions)

说明：  Mt4跟单ea，无限制版本。同向跟单。没有限制时间，地段，账户，还有平台。只要在发送端挂好EA，在接收单设置账户就可以。非常好用。(MT4 with EA, unlimited version. Same direction single. There is no time limit, location, account, or platform. As long as the EA is hung at the sending end, the account can be set at the receiving order. Very easy to use.)

说明：  本书是一本"围绕练习来学习”的图书，特别适合作为大专院校的实验教材或者是自学用书。全书涉及的内容除了LabVIEW的基本编程概念和方法之外，还有大量的章节讨论DAQ的使用及LabVIEW的应用。全书共分13章，内容包括while循环与波形图、for循环与波形图、MathScript模型与XY图、DAQ辅助数据采集、数据文件与字符串、移位寄存器、case结构、数据独立性与序列结构、vi分析、使用DAQmx vi的数据采集与生成、PID温度控制项目、独立仪器控制等。(This book is a "learning around practice" book, especially suitable for college experimental teaching materials or self-study books. In addition to the basic programming concepts and methods of LabVIEW, there are also a large number of chapters to discuss the use of DAQ and the application of LabVIEW. The book is divided into 13 chapters, including while loop and waveform diagram, for loop and waveform diagram, MathScript model and XY diagram, DAQ auxiliary data acquisition, data file and string, shift register, case structure, data independence and sequence structure, VI analysis, data acquisition and generation using daqmx VI, PID temperature control project, independent instrument control, etc.)

说明：  基本遗传算法和模拟退火算法，可扩展，运行效率高，可以广泛应用(Basic genetic algorithm and simulated annealing algorithm are scalable, efficient and can be widely used.)

搭建了一个标准的epri7节点的psasp仿真算例，可供初学者用于psasp仿真(A standard psasp simulation example of epri7 node is built for beginners to use for psasp simulation.)

使用最速下降法和共轭梯度法实现优化功能，基于MATLAB平台使用m语言编写(Based on the MATLAB platform, steepest descent method and conjugate gradient method are used to achieve the optimization function with M language.)

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