zoul-Finch-93def14

在MATLAB上用R-K法编程解一次导数方程 有详细的注释(RK method used in the MATLAB programming solution on the first derivative of equation has detailed notes)

说明：  能够用来求解部分关利用遗传算法的优化模型。。(Relations can be used to solve some of the optimization model using genetic algorithms. .)

说明：  ex2_1 利用Z变换求系统输出 ex2_2 求DFT ex2_3 求序列的圆周移位 ex2_4 求序列的圆周卷积 ex2_5 简单的DFT实例 (ex2_1 the use of Z transform ex2_2 demand for system output DFTex2_3 seeking ex2_4 shift sequence for the circumference of the circular convolution sequence ex2_5 simple example DFT)

船舶操纵运动仿真模型之风力模型研究（根据经验公式得出）(Research ship motion simulation model of the wind model manipulation (based on empirical formulas derived))

说明：  希尔伯特黄变换，经验模态分解，边际谱，超级全代码，可以出图可以自由更换信号(Hilbert Huang transform, empirical mode decomposition, marginal spectrum, super full code, mapping and free signal replacement)

教材：电力电子和电力拖动控制系统的MATLAB仿真(P140-P275)(book about motor)

提取信号的小波细节系数，对原始信号进行3层分解，分别提取每一层信号的细节系数。(To extract the signal wavelet detail coefficients of the original signal is 3 layers of decomposition, respectively to extract the signal the detail coefficients of each layer)

合成孔径雷达 距离多普勒算法 RDA sar距离多普勒算法RDA sar距离多普勒算法RDA(sar RDA)

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.)

计算卷积 计算卷积(Calculation of convolution calculation of convolution)