pt_line

Knut - Art of programming

Splash Window. A windows program demo.

有源电力滤波器在产生指令电流时，基于瞬时无功功率的检测方法需要用到低通滤波器，本程序能够实现滑动滤波！(APF generating command current, based on instantaneous reactive power detection methods need to use a low-pass filter, the program can achieve sliding filter!)

非常经典的基于模糊信息处理的数据融合方法研究.用MATLAB 实现的。 请大家好好学习(Very classical information processing based on fuzzy data fusion method. Using MATLAB realization. Please study hard)

由Patton开发的由MATLAB实现的COPULA源代码(By Patton developed by MATLAB source code to achieve COPULA)

去处干涉条纹最有效的频域消除法，非常实用，效果极佳。(supress grid by frequency domain, very practical with high efficency)

用matlab画了一个小动物，利用了plot，sina，cos等 函数(use Matlab drew a small animal )

五种常见去隔行算法在MATLAB平台上的实现流程(The implementation process of the five common de-interlacing algorithm in the MATLAB platform)

- XCS for Dynamic Environments + Continuous versions of XCS + Test problem: real multiplexer + Experiments: XCS is explored in dynamic environments with different magnitudes of change to the underlying concepts. +Reference papers: H.H. Dam, H.A. Abbass, C.J. Lokan, Evolutionary Online Data Mining – an Investigation in a Dynamic Environment. 2005, accepted for a book chapter in Springer Series on Studies in Computational Intelligence H.H. Dam, H.A. Abbass, C.J. Lokan, Be Real! XCS with Continuous-Valued Inputs. IWLCS 2005, (International Workshop on Learning Classifier Systems). Washington DC, June 2005.(- XCS for Continuous Dynamic Environments Test versions of XCS problem : real multiplexer Experiments : XCS is explored in dynamic environments with di fferent magnitudes of change to the underlying concepts. Reference papers : H. H. Dam, H. A. Abbass, C. J. Lokan. Evolutionary Online Data Mining-an Investiga tion in a Dynamic Environment. 2005. accepted for a book chapter in Springer Series o n Studies in Computational Intelligence H.H. D am, H. A. Abbass, C. J. Lokan. Be Real! XCS with Continuous- Valued Inputs. IW LCS 2005. (International Workshop on Learning Classifi er Systems). Washington DC, June 2005.)

Fast Gaussian Convolution with Recursive Filtering.