ARMAtest

新发表论文：New Local Segmentation Model for Images with Intensity Inhomogeneity，附源码，欢迎下载(New papers: New Local Segmentation. The Model for Images with of Intensity Inhomogeneity attached source are welcome to download)

基于小波变换的mallat算法，用于检测信号的奇异性，实现励磁涌流的鉴别(Based the wavelet transform mallat algorithm for detection signal singularity inrush current identification)

用matlab实现knn时的ft计算过程(Calculated using ft matlab realization process when knn)

使用matlab通过fdtd实现一维到三维的光子晶体模拟(Using MATLAB FDTD to achieve through the one-dimensional to three-dimensional simulation of photonic crystal)

A sourcecode for train adeline algorithm.

人脸表情识别中的2DPCA方法，非特定人识别。(Facial Expression Recognition in 2DPCA method, non-specific Recognition.)

machine learning中最经典的图书(The most classic machine books learning)

说明：  该模型主要模拟蓄电池及其电池管理系统，即BMS，其中包括充放电电流计算、电池温度等，可用于电动汽车、燃料电池汽车模拟，也可用于电池建模计算(The model mainly simulates the battery and its battery management system, namely BMS, including charge and discharge current calculation, battery temperature, etc., which can be used for electric vehicle and fuel cell vehicle simulation, and can also be used for battery modeling calculation.)

Digital Predistortion of Nonlieaner RF Power Amplifier with Memory Effects. This M code simulates a DPD technique linearization for a AB-class nonlinear HPA with memory effects. Here we consider a memory polynomial predistorter to modeling nonlinearity characteristics and memory effects of a HPA.Simulation procedures divided into three separate parts: 1)Analog imperfection compensation for the direct upconversion transmitter 2)Design a DPD based on a memory polynomial predistorter 3)Performance evaluation via a number of parameters such as:EVM,PSD,SNR,

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)