MA

基于Matlab平台的绘制M分形图的一个小代码，希望大家通过学习能优化代码(platform based on the mapping of Matlab M fractal images of a small code, we hope that through learning can be optimized code)

潮流计算程序,用Ｍ语言实现简单电力系统潮流计算，已通过编译(powerflow)

介绍了matlab语言的一些基础知识 对刚刚开始学习matlab的同学很有用的哦 (Matlab language of the basic knowledge has just begun to study the students very Matlab the oh)

说明：  jack and jill went upto hill

《MATLAB 揭秘》为学习这种功能强大的数学计算工具提供了高效的、启发性的方法。 本书使用简单易行的风格，一开始介绍了 MATLAB 基础，你将学习到如何绘制图象，求解代数方程及计算积分，也将学习到如何求解微分方程， 求 ODE 的数值解， 使用特殊函数工作。 本书含有几百个例子及其详细解说过程，章末带有习题，书末还带最终测试题目，一句话，本书给予你的是 MATLAB 精华。 本自学指导书提供了： z 上手 MATLAB的最快方法 z 几百个带有解答过程的工作实例 z 覆盖了MATLAB7 z 每章章末配有习题，提高学习质量，查漏补缺。 z 书末附有最终测试题 z 为学习和工作节省时间提高效率 《MATLAB 揭秘》对初学者很容易，对高手有挑战性，是通往计算精度的捷径。("MATLAB Reveal" to learn the powerful mathematical tools to provide efficient and enlightening way. This book uses a simple style, the beginning describes the basis of MATLAB, you will learn how to draw images, solving algebraic equations and calculating points, will also learn how to solve differential equations, find the numerical solution of ODE, the use of special function work. This book contains hundreds of examples and a detailed explanation of the process, with the end of the chapter exercises, end of the book is also the ultimate test subject with a word, this book gives you the essence of MATLAB. This book provides a self-study guide: z fastest way to get started MATLAB z hundreds of examples of the work process with a solution z covers MATLAB7 z chapter at the end of each chapter with exercises to improve the quality of learning, leak filled. z end of the book title with a final test z for the learning and work to save time and improve efficiency "MATLAB Re)

the most chatoc attractors for encryption and other application

LDPC code construction

Innitialize fitness function

Testing functions with double-byte names.

说明：  该工具包提供了一个用于通过算法、预训练模型和应用程序来设计和实现深度神经网络的框架。您可以使用卷积神经网络（ConvNet、CNN）和长短期记忆 (LSTM) 网络对图像、时序和文本数据执行分类和回归。应用程序和绘图可帮助您可视化激活值、编辑网络架构和监控训练进度。(The toolbox provides a framework for designing and implementing deep neural networks through algorithms, pre training models and applications. You can use convolutional neural networks (convnet, CNN) and long and short term memory (LSTM) networks to perform classification and regression on image, temporal, and text data. Applications and graphics help you visualize activation values, edit network architecture, and monitor training progress.)