生物散斑

一个表,完全用FLASH制作的,效果非常的好,欢迎大家分享-a table entirely with Flash production, the effect was very good, we are happy to share

自治系统的Poincare截面的matlab程序实现-Autonomous System Poincare cross-section of the matlab program

说明：  实现通讯，以太网和欧姆龙plc之间通过网线进行通讯，很好很强大(To achieve communication, Ethernet and OMRON PLC through the network line to communicate, very good and powerful)

说明：  利用python实现的基础bp神经网络，未使用tensorflow，按照原理自主实现(The basic BP neural network implemented by python, without tensorflow, is implemented autonomously according to the principle)

说明：  搭建双向DC-DC双电路，实现直流微网并网运行蓄电池恒压充放电(A bidirectional DC-DC dual circuit is built to realize the constant voltage charge and discharge of the DC micro-grid connected to the grid)

说明：  jQuery自适应全屏NER焦点图是一款效灯片代码，代码简洁。(JQuery adaptive full-s focus map is a very goR slide code, the code is concise.)

算法优化非常好，几乎没有循环，有借鉴意义哦，毕设内容，高光谱图像基本处理。( Algorithm optimization is very good, almost no circulation, There are reference Oh, Complete set content, basic hyperspectral image processing.)

说明：  找到C++大学教程第九版的电子书，服务于学生及C++初学者(Find out the PDF of the ninth edition of C + + university course for students and C + + beginners)

利用恒虚警检测概率检测方法（CFAR）添加了一定噪声的目标信号中目标的个数(The process of CFAR in target detection)

共轭梯度法（Conjugate Gradient）是介于最速下降法与牛顿法之间的一个方法，它仅需利用一阶导数信息，但克服了最速下降法收敛慢的缺点，又避免了牛顿法需要存储和计算Hesse矩阵并求逆的缺点，共轭梯度法不仅是解决大型线性方程组最有用的方法之一，也是解大型非线性最优化最有效的算法之一。 在各种优化算法中，共轭梯度法是非常重要的一种。其优点是所需存储量小，具有步收敛性，稳定性高，而且不需要任何外来参数。(The Conjugate Gradient method is a method between the steepest descent method and the Newton method. It only needs to use the first derivative information, but overcomes the shortcoming of the steepest descent method and avoids the need for the Newton method to store. And the disadvantage of calculating the Hesse matrix and inverting, the conjugate gradient method is not only one of the most useful methods for solving large linear equations, but also one of the most effective algorithms for solving large-scale nonlinear optimization. Among various optimization algorithms, the conjugate gradient method is a very important one. The advantage is that the required amount of storage is small, step convergence, high stability, and no external parameters are required.)