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This program implements equation (2.26) using the waterfilling algorithm

说明：  统计自适应信号处理的matlab程序，学习统计自适应信号处理的朋友可以参考(statistics adaptive signal processing Matlab procedures, statistical study of adaptive signal processing can refer a friend)

matlab学习资料，利于新生入门学习，能够更好的应用matlab(matlab learning materials, which will help new students started to learn, to better application of matlab)

说明：  利用matlab提取出频域和时域信号的29个特征，主运行文件feature_extraction，fre_statistical_compute和time_statistical_compute分别提取频域和时域的特征，生成的29个特征保存在生成的feature矩阵中。(Using MATLAB to extract 29 features of frequency-domain and time-domain signals, the main running files feature extraction, frequency-domain and time-domain features are extracted respectively, and the 29 features are saved in the generated feature matrix.)

说明：  matlab车桥耦合，计算分析，便于自学(Matlab vehicle bridge coupling)

数值计算方法 龙格现象+解决（切比雪夫点的定义）(Numerical method Runge phenomenon+ Solution (Chebyshev point definition))

MATLAB编写的遗传算法，采用二进制编码，有说明文档(Genetic algorithm written in MATLAB, using binary code, there is documentation)

带有相关性MIMO信道仿真程序，仿真的是MIMO平坦衰落信道。(associated with MIMO channel simulation program, based on the MIMO flat fading channel.)

应用MATLAB的YAMLIP工具箱求解简单的机组组合问题(YAMLIP unit commitment problems)

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.)