jiexiangguan_LMS_multi_input

In machine learning, support vector machines (SVMs, also support vector networks[1]) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that assigns new examples into one category or the other, making it a non-probabilistic binary linear classifier. An SVM model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall on.

it s backpropagation neural network for iris data set

该代码为盲源分离中的相对牛顿法，收敛性能稳定，分离效果优越，值得借鉴。(The code for the blind source separation of the relative Newton method, convergence properties of stability and separation efficiency is superior, is worth learning from.)

fsk的实现程序！很好的。大家看看。 fsk的实现程序！很好的。大家看看。(the realization of the procedure fsk! Very good. Let us take a look. the realization of the procedure fsk! Very good. Let us take a look.)

插值与拟合的相关功能，使用Matlab软件，其中包含源程序和PPT解说(Interpolation and fitting)

这是大学毕业时设计的失真度检测仪论文，由模拟陷波器实现的剔除几波，如果改进的话，可以用数字滤波器实现，不过成本更高(It is designed for university graduation thesis distortion detector by analog notch filter removed to achieve a few waves, if improved, you can use digital filters to achieve, but higher costs)

These are used for speech test signals

复杂网络的统计参数的计算，在matlab中建立了一个m文件(The calculation of statistical parameters of complex network, established a m files in matlab)

人脸图像的2级小波分解，用MATLAB实现。可得到人脸图像的二级小波分解图。使用时只需把图片名称改成自己所具有的即可(Face feature extraction)

bp_innerloop.m Inner loop of the backpropagtion learning algorithm. One hidden layer. Uses tanh as the transfer function. Uses the following global variables for input and/or output: Inputs1 - input patterns Desired - desired output patterns LearnRate - learning rate parameter Momentum - momentum parameter DerivIncr - increment to the derivative of the transfer function (Fahlman s trick typical value 0.2) Weights1 - first weight layer (updated by this routine) Weights2 - second weight layer (updated by this routine) deltaW1 - initialize to 0 before first call deltaW2 - initialize to 0 before first call TSS - total sum-squared error (set by this routine) Recurrent state