matlabJIEPING

生成白噪声信号：包括采样频率，最大和最小频率，时间长度，幅值等要求的信号。(Generated white noise signal: including sampling frequency, maximum and minimum frequency, duration, amplitude and other requirements of the signal.)

modulation coding for communication channel

用于PF算法研究的5中基本算法及相应的实现程序，都可以直接运行，对于学习PF非常有帮助(For PF algorithm research 5 basic algorithm and the corresponding implementation procedures, can be run directly, is helpful for learning PF)

matlab生成LDPC的H矩阵，消除4环，同时列重为3，很好的例子(based matlab generate H matrix of ldpc)

Implementation of Quadrature Amplitude Modulation-Demodulation

传统的K-medoids聚类算法的聚类结果随初始中心点的 不同而波动，且计算复杂度较高不适宜处理大规模数据集； 快速K-medoids聚类算法通过选择合适的初始聚类中心改进 了传统K-medoids聚类算法，但是快速K-medoids聚类算法 的初始聚类中心有可能位于同一类簇。为了克服传统的K- medoids聚类算法和快速K-medoids聚类算法的缺陷，提出 一种基于粒计算的K-medoids聚类算法。(The traditional K-medoids clustering algorithm clustering results with different initial center points and volatility, and high computational complexity is not suitable for processing large data sets; K-medoids clustering algorithm by choosing proper initial cluster centers to improve the traditional K-medoids clustering algorithm, but the initial cluster center of K-medoids clustering algorithm can be located in the same cluster. In order to overcome the shortcomings of the traditional K- medoids clustering algorithm and the fast K-medoids clustering algorithm, a K-medoids clustering algorithm based on granular computing is proposed.)

matlab pmsm simulink example

仿真流量预测模型，模拟产生实际流量由小变大，并实时预测下一时刻的流量信息。(Simulation flow forecasting model to simulate a real flow from small to big, and real-time traffic information predict the next moment.)

说明：  该算法采用如下共聚类结构进行多重聚类： 1.优化地将数据集划分为多个视图。在每个视图中，对象和特征都是群集的。 2.同时分析了三类特征：高斯特征、泊松特征和范畴特征。 3.自动推断视图和簇的数量。(This algorithm carries out multiple-clustering with co-clustering structures as follows: 1.Optimally divides datasets into several views. In each view, both objects and features are clustered. 2.Simultaneously analyzes three types of features: Gaussian, Poisson and categorical features. 3.Automatically infers the number of views and clusters.)

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