遗传算法

svm with matlab code

It includes coding for walsh code encoding in matlab.The Walsh functions can be generated (or computed) by many methods. We can construct the Walsh functions by: • Using Hadamard matrices • Exploiting the symmetry properties of Walsh functions themselves. • The Hadamard matrix is a square array of plus and minus ones, {+1, -1}, whose rows and columns are mutually orthogonal

16qam调制解调，包括建立仿真模型等等(16qam modem modulation)

计算sigma-delta调制器输出码流的信噪比。(calculate the ratio of signal to noise of sigma-delta modulator.)

function [x,n] = stepseq(n0,n1,n2)

duffing振子非线性系统的分叉图和映射图程序，简单易学(Bifurcation diagram of nonlinear system of duffing oscillator is simple and easy to learn)

自适应波束形成器（包括空域谱矩阵的估梯度实现算法、梯度算法、LMS算法、信号子空空间的检测等）(Adaptive beamforming (including the airspace spectral matrix of the estimated gradient algorithm, the gradient algorithm, LMS algorithm, the detection of the signal subspace space, etc.))

实现随钻测量中各个角度的校正系数的计算方法，以实现正确角度校正，实现井眼轨迹控制(To achieve with measurement while drilling angle correction factor calculated in order to achieve the correct angle correction, the well trajectory control)

M/N逻辑法航迹起始程序，仿真环境参照《雷达数据处理及应用》P99页，程序简单明了，适合初学者(M/N logic method of track initiation procedures, simulation environment reference radar data processing and application P99 page, the procedure is simple and clear, suitable for beginners)

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.)