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在Matlab环境下，关于近似熵的计算程序()

CA code generator for gps

An example C-file S-function for accessing simulink signals * without using the standard block inputs. In this case, a floating * bar-plot style scope is implemented. The signals that are selected * on the diagram (via the mouse) are displayed by the scope. The * companion file <matlabroot> oolboxsimulinklocksarplotm.m * receives the signal data and plots it. See the demo model, * <matlabroot> oolboxsimulinksimdemossfcndemo_barplot.mdl.

This code shows in a simple way the implementation of 2 dimention finite difference time domain simulation. We also implement perfectly match layer at the border.

在假设负荷预测、风机和光伏未来出力已完全准确的情况下（允许弃风弃光），采用改进PSO算法对某含有风机、光伏、以及常规负荷的微电网未来24 小时的出力调控进行日前优化，以实现经济性最优(Under the assumption that the load forecasting, the wind turbine and the future power of the photovoltaic power are completely accurate (allowing for abandonment of the wind and light), the improved PSO algorithm is used to optimize the output of a microgrid containing the fan, photovoltaic, and conventional load for the next 24 hours in order to achieve the optimal economy.)

基于MATLAB 的数值分析方面的源代码，非常实用，对于数值分析方面得研究来说(MATLAB-based numerical analysis of the source code, very useful for the numerical analysis for a study)

subtractive clustering

说明：  数据挖掘算法，fuzzy-K-means聚类算法源代码，用于模糊聚类分析(data mining algorithms, fuzzy-K-means clustering algorithm source code for Fuzzy Cluster Analysis)

模拟2个独立窄带信号分别以20°，60°的方向入射到均匀线阵上信号间互不相关，与噪声相互独立，噪声为理想高斯白噪声，阵元数为10，阵元间距为入射信号波长的1／2，信噪比为20dB，快拍数分别为5，50，200。(Simulate two independent narrowband signals in the direction of 20 °, 60 ° of the incident on the ULA between signal uncorrelated with independent noise, the noise is ideal Gaussian white noise, the array element number 10, the array element spacing of the incident 1/2 wavelength of the signal to noise ratio of 20dB, the number of snapshots 5,50,200 respectively.)

用于分类的主判别变量算法(principal discriminant variate algorithm) ( principal discriminant variate algorithm for two classes, x1 and x2. x1 is the training samples in the 1st class with each column being the spectrum characterizing the corresponding sample. x2 is the training samples in the 2nd class with each column being the spectrum characterizing the corresponding sample. lamda is the penalty weight defining the balance between FLDA and PCA. dvnum is the number of principal discriminant variates to be computed. A defaul value for dvnum is 2. dv is the principal discriminant variates. scores is the matrix with each row being the scores of the samples on the corresponding principal discriminant variate. xmean is the mean spectra of the sample, which was used for mean-centering the data in training and prediction. )