CARS_Characteristic-band-extraction

说明：  一些小波分析的有用程序 能帮助大家更好的理解一些小波变换和小波的主要功能(wavelet)

Applied numerical methods using MATLAB

说明：  一个双向LSTM程序 Long Short Term 网络—— 一般就叫做 LSTM ——是一种 RNN 特殊的类型，可以学习长期依赖信息。LSTM 由Hochreiter & Schmidhuber (1997)提出，并在近期被Alex Graves进行了改良和推广。在很多问题，LSTM 都取得相当巨大的成功，并得到了广泛的使用。 LSTM 通过刻意的设计来避免长期依赖问题。记住长期的信息在实践中是 LSTM 的默认行为，而非需要付出很大代价才能获得的能力！ 所有 RNN 都具有一种重复神经网络模块的链式的形式。在标准的 RNN 中，这个重复的模块只有一个非常简单的结构，例如一个 tanh 层。(A bidirectional LSTM program Long short term network, commonly known as LSTM, is a special type of RNN that can learn long-term dependent information. LSTM was proposed by Hochreiter & schmidhuber (1997) and recently improved and promoted by Alex graves. In many problems, LSTM has achieved great success and has been widely used. LSTM is designed to avoid long-term dependency. Remember that long-term information is the default behavior of LSTM in practice, not the ability to acquire it at a great cost! All RNNs have a chained form of repetitive neural network modules. In the standard RNN, this repetitive module has only a very simple structure, such as a tanh layer.)

svm已经广泛用于解决分类和回归问题。 此工具箱是由South ampton大学的S. R. Gunn编写的Matlab SVM Toolbox。该工具箱运行在MATLAB环境下,由许多用m语言编写的脚本文件和函数组成,为SVM 技术的工程化、实用化提供了一个良好的平台。(South ampton university svm toolbox)

拟牛顿法的matlab应用，提供matlab程序，数值分析核心方法(Quasi-Newton method matlab applications, providing matlab program, the core numerical analysis method)

说明：  matlab code from in the problem in numerical caculation.

这是我先根据MATLAB设计的一个FIR滤波器，然后c语言实现，是二阶的，非常好用。(This is my first one under the MATLAB FIR filter design, and then c languages to achieve, is the second order, quite well.)

power flow analysis using newton raphson method

用递推式法求解；用二分法，牛顿迭代法，迭代法求解方程的近似根(Using recursive method for solving with the dichotomy, Newton iteration method, iteration method for solving the approximate root)

This tutorial is the next of before, you will learn a few of the basic functions of Matlab. First we will start working with basic mathematical functions, setting variables, and generating time series.