Codes

这是一个Z变换和Z反变换的matlab程序。(This is an anti-Z transform and Z transform matlab procedures.)

超松弛迭代法编写matlab程序求解槽空间电位分布问题(Overrelaxation iterative method to write matlab program for solving groove space potential distribution problems)

This is a code for Uniform Linear Array Data

Vim用户手册中文版72，非常好的VIM使用手册，方便辅助开发(Vim user manual Chinese version of VIM 72 very good manual, easy to supporting development)

说明：  matlab与 人工神经网络2007 配套源程序(matlab and artificial neural network supporting source, 2007)

matlab编的数独游戏，挺好玩的，大家一起研究一下(Sudoku)

Chapter 2. The Solution of Nonlinear Equations f(x) = 0 Algorithm 2.1 Fixed Point Iteration Algorithm 2.2 Bisection Method Algorithm 2.3 False position or Regula Falsi Method Algorithm 2.4 Approximate Location of Roots Algorithm 2.5 Newton-Raphson Iteration Algorithm 2.6 Secant Method Algorithm 2.7 Steffensen s Acceleration Algorithm 2.8 Muller s Method Algorithm 2.9 Nonlinear Seidel Iteration Algorithm 2.10 Newton-Raphson Method in 2-Dimensions

gilber strang files Computational Science and Engineering

Relief算法中特征和类别的相关性是基于特征对近距离样本的区分能力。算法从训练集D中选择一个样本R，然后从和R同类的样本中寻找最近邻样本H，称为Near Hit，从和R不同类的样本中寻找最近样本M，称为Near Miss，根据以下规则更新每个特征的权重： 如果R和Near Hit在某个特征上的距离小于R和Near Miss上的距离，则说明该特征对区分同类和不同类的最近邻是有益的，则增加该特征的权重；反之，如果R和Near Hit在某个特征上的距离大于R和Near Miss上的距离，则说明该特征对区分同类和不同类的最近邻起负面作用，则降低该特征的权重。(The correlation between feature and category in Relief algorithm is based on distinguishing ability of feature to close sample. The algorithm selects a sample R from the training set D, and then searches for the nearest neighbor sample H from the samples of the same R, called Near Hit, and searches for the nearest sample M from the sample of the R dissimilar, called the Near Miss, and updates the weight of each feature according to the following rules: If the distance between R and Near Hit on a certain feature is less than the distance between R and Near Miss, it shows that the feature is beneficial to the nearest neighbor of the same kind and dissimilar, and increases the weight of the feature; conversely, if the distance between R and Near Hit is greater than the distance on R and Near Miss, the feature is the same. The negative effect of nearest neighbor between class and different kind reduces the weight of the feature.)

说明：  根据归一化后的原始数据曲线，运用自组织神经网络进行藻种识别(According to the normalized data,we identify the HABs using the SOM.)