MatlabAlgors

地震CT射线追踪CPP,考虑了深度临界值的情况(Earthquake CT ray tracing CPP, consider the case of the depth threshold)

SVM matlab的源代码，用于建立支持向量机模型(code for svm)

用simulink对风电场进行建模分析得到功率以及电流的仿真图(This example uses an initial state vector to start the simulation steady-state. When you make changes to the model (add, delete, rename blocks, etc), the initial state vector needs to be regenerated or disabled, otherwise Simulink signals an error when the simulation is started. To disable the use of initial state vector for this model, go to the Data Import/Export section of the Model Configuration Parameters tool and uncheck the Initial state option under the Load workspace section. To regenerate the initial state vector after a change to the model, follow the instructions given in the initialization file for this model. Type open( init_power_wind_dfig ) at the matlab prompt to view this file.)

图像压缩matlab的霍夫曼编码huffman

用牛顿迭代法求方程 f(x)=x³ -x² -1=0 在x。=1.5附近的一个实根。取§=0.000001，取最大迭代数为60. (With Newton iteration equation for f (x) = x ³-x ² -1 = 0 in x. = 1.5 near the root of a real. Take § = 0.000001, take the largest number of 60 iterations.)

Matlab中基于dq变换的锁相环仿真研究.(Matlab based on dq transformation of phase-locked loop simulation.)

说明：  主要是基因识别的 matlab程序 一种全新的算法(Gene identification is mainly a new algorithm matlab program)

Matlab中生成MIF格式的文件，满足Quartus中载入数据需要(Matlab generated MIF file formats to meet the data needs to load Quartus)

详细介绍了BP神经网络和RBF神经网络的分类能力(Details of the BP neural network and RBF neural network classification ability)

这篇文章是盲信号处理，因为是英文文章，所以大家看内容就好了。(Principal component analysis (PCA) is a mainstay of modern data analysis- a black box that is widely used but poorly understood. The goal of this paper is to dispel the magic behind this black box. This tutorial focuses on building a solid intuition for how and why principal component analysis works furthermore, it crystallizes this knowledge by deriving from simple intuitions, the mathematics behind PCA . This tutorial does not shy away from explaining the ideas informally, nor does it shy away from the mathematics. The hope is that by addressing both aspects, readers of all levels will be able to gain a better understanding of PCA as well as the when, the how and the why of applying this technique.)