资源列表
LM_sample
- Levenberg Marquardt算法的matlab代码,简洁无误,结构清晰,便于快速学习。- LevenbergMarquardt algorithm matlab code, simple and correct, clear structure, easy to learn quickly.
hiesing_v62
- 加入重复控制，包括最后计算压缩图像的峰值信噪比和压缩效果的源码,采用了小波去噪的思想。- Join repetitive control, Including the final calculation of the compressed image peak signal to noise ratio and compression of the source, Using wavelet denoising thought.
Hex2Dec
- 将十六进制数据转换为十进制数据。高效、准确，整数采用双精度格式-Converts hexadecimal data to decimal data. Efficient, accurate, integer with double precision format
Source1
- 逆迭代法算特征向量和特征值 由起始向量开始计算 逆迭代法算特征向量和特征值 由起始向量开始计算- U9006 u8FED u4EE3 u6CD2 u7B97 u7279 u5F81 u5411 u91CF u548C u7279 u5F81 u503C u7531 u8D77 u59CB u5411 u91CF u5F00 u59CB u8BA1 u7B97
nsga2code
- C语言开发的基于基因遗传算法进行多目标优化设计-C language development based on genetic genetic algorithm for multi-objective optimization design
ER_Pk
- (fortran)构建无向ER网络，计算度分度-Codes are written by fortran,aiming to building ER model and calculating degree distribution
ER_Cp
- 用fortran写的。构建无向ER网络，以及计算该无向ER网络的聚集系数。-Codes are written by fortran,aiming to building undirected ER models and calculating clustering ccoefficient.
WS_PK
- 用fortran写的，构建无向WS模型，并且计算度分布。-Codes are written by fortran, aiming to build undirected WS model and calculate degree disrtribution.
WS_CP
- 用fortran写的，用来构建无向WS网络以及计算聚集系数。-Codes written by fortran, aiming to build undirected WS model and calculate clustering coefficient.
rrvkm
- IMC-PID是利用内模控制原理来对PID参数进行计算，ICA（主分量分析）算法和程序，加入重复控制。- The IMC- PID is using the internal model control principle for PID parameters is calculated, ICA (Principal Component Analysis) algorithm and procedures, Join repetitive control.
lei_xq73
- 用于信号特征提取、信号消噪，matlab程序运行时导入数据文件作为输入参数，利用最小二乘算法实现对三维平面的拟合。- For feature extraction, signal de-noising, Import data files as input parameters matlab program is running, Least-squares algorithm to fit a three-dimensional plane.
tlbo20170207_ok
- 教与学算法，局部优化算法，收敛快，精度高。可与差分进化等全局寻优算法配合使用，这个算法的特点就是局部收敛速度快。-56/5000 Jiào yǔ xué suànfǎ, júbù yōuhuà suànfǎ, shōuliǎn kuài, jīngdù gāo. Kě yǔ chāfēn jìnhuà děng quánjú xún yōu suànfǎ pèihé shǐyòng, zhège suànfǎ de tèdiǎn jiùshì júbù shōuliǎn sùdù kuài.