jiaoyan

通过迭代计算方法计算波浪波长的程序，可以计算不同周期波浪由深水传向浅水，不同深度波长的变化。(caculate the wavelength ,you should input the depth and period)

时间序列分析，需要的额下来看看，求高人指导(Time series analysis, the amount needed down to look at, seek expert guidance)

基于小波变换的数字音频水印算法 水印嵌入源程序 (With the rapid growth of computers, network and multimedia technologies, powerful and low cost computers allow people to easily create and copy digital products of multimedia. The Internet has made it easy to distribute the information at very low cost. However, these technologies also can be illegally copied, modified and redistributed multimedia data without regarding for intellectual property rights ownership. )

gravititional search algorithm

用于六维外弹道的计算,可以计算六自由度的外弹道(using for 6 dof blastic)

这是matlab的代码，将cnn进行三次池化的代码，可以作为学习卷积神经网络的入门例子(This is a matlab code that will perform three pooled cnn code can be used as a convolution neural network learning introductory example)

Air traffic controller

利用小波分析对污染信号消噪以恢复原始信号(Using wavelet analysis of contamination signal to recover the original signal de-noising)

说明：  相关向量机（Relevance Vector Machine，简称RVM）是Micnacl E.Tipping于2000年提出的一种与SVM（Support Vector Machine）类似的稀疏概率模型，是一种新的监督学习方法。 它的训练是在贝叶斯框架下进行的，在先验参数的结构下基于主动相关决策理论（automatic relevance determination，简称ARD）来移除不相关的点，从而获得稀疏化的模型。在样本数据的迭代学习过程中，大部分参数的后验分布趋于零，与预测值无关，那些非零参数对应的点被称作相关向量（Relevance Vectors），体现了数据中最核心的特征。同支持向量机相比，相关向量机最大的优点就是极大地减少了核函数的计算量，并且也克服了所选核函数必须满足Mercer条件的缺点。(Relevance Vector Machine (RVM) is a sparse probability model similar to SVM (Support Vector Machine) proposed by Micnacl E. Tipping in 2000. It is a new supervised learning method. Its training is carried out under the Bayesian framework. Under the structure of prior parameters, it is based on Automatic Relevance Determination (ARD) to remove the irrelevant points, so as to obtain the sparse model. In the iterative learning process of sample data, the posterior distribution of most parameters tends to zero, which is independent of the predicted value. The points corresponding to non-zero parameters are called Relevance Vectors, which represent the most core features of the data. Compared with support vector machine, the biggest advantage of correlation vector machine is that it greatly reduces the computation amount of kernel function, and also overcomes the shortcoming that the selected kernel function must meet Mercer's condition.)

最原始的灰色预测，matlab程序，可直接使用(Most original gray forecast, matlab program, can be used directly)