bfilter2

矩阵实例，有题目和相应的代码及仿真图像，方便初学者轻松认识matlab编程思想(Matrix instance, there are topics and the corresponding code and simulation images to facilitate easy understanding of matlab programming ideas for beginners)

实现MMC的三相闭环控制和尖锐脉冲去除源代码(Realization of three-phase closed-loop control of MMC and source code removal of sharp pulse)

说明：  李雅普诺夫稳定性分析，包含几种典型用例，调整ode45中背调函数，即可实现不同案例的李雅普诺夫稳定性分析。(Lyapunov stability analysis, including several typical cases, can achieve Lyapunov stability analysis of different cases by adjusting the back tone function in ode45.)

用matlab语言对汽车动力经济性进行仿真(very good)

matlab code for adding and splitting frame to the video

说明：  关于Z变换的各种例子 帮助我们较为简单的学习 取得较快的进步(About Z transform of a variety of examples to help us study a relatively simple rapid progress)

本书由基础知识和应用两大部分组成，基础知识详细介绍了MATLAB基础准备与入门、MATLAB的基本使用方法、数组和数组运算、字符串单元数组和结构、数值计算功能、符号运算、数据和函数的可视化、MATLAB程序设计、图形用户界面设计、Simulink仿真基础；在应用方面，介绍了MATLAB在信号与系统、数字信号处理和通信原理中的应用等内容。本书在介绍方式上使用了大量图片、例子，具有较好的操作指导作用，通过实例帮助读者加深理解。(Benshu basic knowledge and applied by the two major components, introduces the basics of preparation and entry based on MATLAB, MATLAB s basic use, arrays and array operations, array of string units and structures, numerical calculations, symbolic computation, data, and visual function, MATLAB programming, graphical user interface design, Simulink based simulation In the application, describes a MATLAB in signal and systems, digital signal processing and communication theory in the application of content. This book describes the way in the use of a large number of pictures, examples, and has good operating guide, an example to help readers better understanding.)

基于matlab的小波算法，给出了一个完整的算例，初学者可以很好的学习。(Based on the the matlab wavelet algorithm is given a complete example, beginners can learn well.)

Histogram of letters and pair of letters from a text file.

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.)