MathematikInformatiker2

Another very good book of FDTD method

实现了PLSA的MTLAB算法，分门别类，较详细。(PLSA algorithm to achieve the MTLAB, categories, more detailed.)

这个程序可以画出一个给定圆心和半径的圆，参数分别是x，y，r。(This program can draw a circle and the radius of a given circle, parameters are respectively the x, y, r)

这个文件是复杂网络的介数介绍的一些细节。(This document is referred to a number of complex networks, introduced some of the details.)

基于四阶统计量的盲源分离算法，它是特征矩阵联合对角化的简称(Fourth-order statistics-based blind source separation algorithm, it is the characteristic matrix referred to as the joint diagonalization)

Matlab code for The Labor-Supply Elasticity and Borrowing Constraints: Why Estimates are Biased 3

提出了一种基于小波变换和模糊集的图像融合算法.其基本思想是: 首先对图像进行小波变换,获得图 像的低频和高频分量 随后在融合过程中, 对低频和高频分量采取不同的融合策略, 即对低频分量采用平均能 量法进行融合,对高频成分利用图像的模糊集, 寻求一个模糊隶属函数作为融合算子进行融合 最后再对融合 后的图像进行小波反变换,重构出融合后的图像.实验结果证明了方法的有效性.(Proposed image fusion algorithm based on wavelet transform and fuzzy sets. The basic idea is: First the image wavelet transform to obtain the low and high frequency components of the image followed by low and high frequency components in the fusion process to take a different the integration of strategy, integration of the low frequency components using the average energy method, the use of high frequency components of the image fuzzy sets, look for a fuzzy membership function as a fusion operator for fusion image fusion wavelet transform and re- structure of fused images. experimental results demonstrate the effectiveness of the method.)

该matlab代码实现了“综合多分支分裂式层次化聚类” (Matlab implementation of Omnibus Multi-Branching Divisive Hierarchical Clustering (OMBDHC) “A competitive omnibus performance criterion for divisive multi-branching hierarchical clustering” by Soeria-Atmadja et al. (submitted). Note that OMB-DHC package is implemented in Matlab code and therefore demands the Matlab core program, as well as the Statistics toolbox of Matlab. Divisive hierarchical clustering (DHC) has emerged as a promising alternative for the identification of sub-structures in multivariate data. Of particular interest is multi-branching DHC, which allows flexible numbers of subclusters at each hierarchical level. One poorly explored issue in multi-branching DHC concerns the actual importance of the two performance criteria (and their associated algorithms) to automatically create clusters and select number of clusters, respectively. Another interesting but hitherto unexplored issue is the possibility to employ a single omnibus performance criterion that guide)

shenasai system control modern

此为双通道的瑞利衰落信道的仿真，使用MATLAB完成。(This is the dual-channel Rayleigh fading channel simulation, the use of MATLAB to complete.)