TM_EFIE

Z源与Boost电路结合的单相全桥逆变器的开环仿真(Z-Source Boost circuit with a combination of single-phase full-bridge inverter open-loop simulation)

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.)

介绍DFT滤波的基本步骤，很规范，很实用(DFT filter introduces the basic steps, very standard, very practical)

本程序的功能是：实现信号从时域到频域的快速傅里叶变换。(Realize the signal conversion from time domain to frequency domain.)

说明：  实现nsga2，交叉和变异算子为二进制编码和多项式变异(Nsga2 is implemented and crossover and mutation operators are binary coded and polynomial variable)

srm 6/4 machin edesign using matlab m file

改进cholesky分解，在cholesky分解基础之上进行改进，不需要分解的矩阵正定条件(Improve the cholesky decomposition, cholesky decomposition in the basis for improvement, positive definite matrix conditions without decomposition)

利用matlab GUI实现的串口编程例子，对于初学者具有参考意义，小波包分析提取振动信号中的特征频率。( Use serial programming examples matlab GUI implementation, For beginners with a reference value, Wavelet packet analysis to extract vibration signal characteristic frequency.)

这是我自己花了很多时间写的一个模式识别源码。奉献出来与感兴趣的朋友分享，绝对可靠。.. ( Dedication out with friends interested in sharing, absolutely reliable. ..)

灰色预测例题。。。。。。。。。。。。。。。。。。。。。。。。。。。(Grey. . . . . . . . . . . . . . . . . . . . . . . . . . .)