nonlinear-oscillation-example

冈萨雷斯数字图像处理配套第八章matlab例程，代码里面有详细解释，适合初学者同步学习。(Gonzalez Digital Image Processing Chapter 8 of the supporting matlab routines, the code inside a detailed explanation of the synchronous learning for beginners.)

可用于转子的动力学分析，包括固有频率的计算和振型的输出。(Can be used for rotor dynamics analysis, including the calculation of the natural frequency and vibration mode output.)

非下采样轮廓波变换MATLAB工具箱，包含DEMO(Nonsubsampled Contourlet transform MATLAB toolbox, including DEMO)

该算法是实现Kmean聚类算法。包含初始聚类中心，聚类成员列表，最终聚类中心，最终聚类中心的距离，方差分析表，每个聚类中的案例数。(The algorithm is a clustering algorithm to achieve Kmean. Contains the initial cluster centers, cluster membership list, the final cluster centers the final cluster centers, ANOVA table, the number of cases in each cluster.)

正态反高斯模型的参数估计并且结合上下文模型的算法研究与实现matlab,这是一个很好的算法！(Normal anti-Gaussian model parameter estimation algorithm context model and combined with the realization of matlab)

模块化多电平转换器的动态分析,详细的介绍了开发步骤和结论(Modular multi-level converter dynamic analysis, a detailed description of the development steps and conclusions)

Z变换经典程序，是经时域变换到幅频域得有力工具(Z transform classic program, pieces by the time domain to the frequency domain a powerful tool)

multi input pv for distributed applications

这是一个关于稀疏高斯过程的matlab源码，可以用于计算测试输入的高斯预测值。( spgp_pred computes the SPGP predictive distribution for a set of test inputs. You need to supply a set of pseudo-inputs or basis vectors for the approximation, and suitable hyperparameters for the covariance. You can use any method you like for finding the pseudo-inputs , with the simplest obviously being a random subset of the data. It is coded for Gaussian covariance function, but you could very easily alter this. It is also fine to use for high dimensional data sets. spgp_lik is the SPGP (negative) marginal likelihood and gradients with respect to pseudo-inputs and hyperparameters. So you can use this if you wish to try to optimize the positioning of pseudo-inputs and find good hyperparameters, before using spgp_pred . I would recommend initializing the pseudo-inputs on a random subset of the data, and initializing the hyperparameters sensibly. Its current limitations are that 1) it is slow and memory intensive for high dimensional data sets 2) it is heavi)

说明：  基于Matpower的IEEE33节点算例配电网潮流计算(A Matpower-based IEEE33 node example for power flow calculation of distribution network)