To_design_a_Band_Stop_Filter_using_Blackman_windo

非常不错的书籍，《微粒群算法》供8章和2个附录，依次上传第一章绪论(Very good books, Particle Swarm Optimization for Chapter 8 and Appendix 2, followed by From Chapter I Introduction)

demonstrates the objects of the System Identification Toolbox.

In this paper, we show how support vector machine (SVM) can be employed as a powerful tool for $k$-nearest neighbor (kNN) classifier. A novel multi-class dimensionality reduction approach, Discriminant Analysis via Support Vectors (SVDA), is introduced by using the SVM. The kernel mapping idea is used to derive the non-linear version, Kernel Discriminant via Support Vectors (SVKD). In SVDA, only support vectors are involved to obtain the transformation matrix. Thus, the computational complexity can be greatly reduced for kernel based feature extraction. Experiments carried out on several standard databases show a clear improvement on LDA-based recognition

说明：  应用了一种简单但有效的图像先验规律即暗原色先验规律，来为单幅图像去雾。(Application of a simple but effective rules that dark color image prior law prior to the single image to the fog.)

对mitbih数据库中心电信号滤波，高通，低通，带通去除工频干扰 (Database centers mitbih signal filtering, high pass, low pass, band-pass removal of the working frequency interference)

四种支持向量机SVM工具箱的分类与回归算法。MATLAB编写(Vector Machine SVM classification and regression algorithm toolbox four support. Written in MATLAB)

matlab simulink 模糊控制器模型(matlab simulink fuzzy controller model)

说明：  cca典型相关分析的多变量版本mcca，典型的一种盲源分离算法(Multivariate version of canonical correlation analysis)

说明：  基于MATLABD的线性二次型最优控制设计(MATLABD based on the linear quadratic optimal control design)

参考文件，MMBT3904 datasheet(References，MMBT3904 datasheet)