svpwm三相逆变器（svpwm_simulink.slx）

【实例简介】 OpenCossan Toolbox 是 Cossan 软件的核心。它以面向对象的方式编程，以提供工具箱的灵活性和可扩展性。事实上，使用这个工具箱，用户和研究人员可以修改预先编写的解决方案序列、探索数据、实施新算法等，换句话说，只需创建自定义工具，就可以解决专门的问题。 该软件提供了最先进和最新的算法，用于对不确定性进行合理量化和传播。此外，由于随机方法通常对计算要求很高，因此需要采用高性能计算以减少分析的执行时间（挂钟时间）。在这方面，OpenCossan 中可用的实现与并行计算功能相结合，以便能够在可行的计算时间内分析现实问题。因此，OpenCossan 已经发展成为从业者执行不确定性量化、基于仿真的可靠性分析、敏感性分析、元建模和基于可靠性的优化的多合一软件包软件。

基于粒子群算法的含风电10机组机组组合程序，可以直接使用

【实例简介】机器学习中的多示例包层次SVM分类算法 【核心代码】Bag_KI_SVM.m KI-SVM ├── Bag KI-SVM │   ├── Bag_KISVM_prediction.m │   ├── Bag_KI_SVM.m │   ├── Find_y.m │   ├── Find_y_linear.m │   ├── Max_Violated_y_set.m │   ├── Readme.htm │   ├── celltomatrix.m │   ├── genIndex.m │   └── normalization_gaussian.m ├── Instance KI-SVM │   ├── Find_y.m │   ├── Find_y_linear.m │   ├── Inst_KISVM_prediction.m │   ├── Inst_KI_SVM.m │   ├── Max_Violated_y_set.m │   ├── Readme.htm │   ├── celltomatrix.m │   ├── genIndex.m │   └── normalization_gaussian.m ├── experiments.m ├── experiments_KISVM_musk1.m ├── libsvm-mat-2.88-MI-svm │   ├── COPYRIGHT │   ├── Makefile │   ├── README │   ├── doc.txt │   ├── heart_scale.mat │   ├── make.asv │   ├── make.m │   ├── read_sparse.c │   ├── read_sparse.mexw32 │   ├── read_sparse.mexw64 │   ├── svm.cpp │   ├── svm.cpp.bak │   ├── svm.h │   ├── svm.h.bak │   ├── svm.obj │   ├── svm_model_matlab.c │   ├── svm_model_matlab.c.bak │   ├── svm_model_matlab.h │   ├── svm_model_matlab.obj │   ├── svmpredict.c │   ├── svmpredict.mexw32 │   ├── svmpredict.mexw64 │   ├── svmtrain.c │   ├── svmtrain.c.bak │   ├── svmtrain.mexw32 │   └── svmtrain.mexw64 └── normedmusk1.mat 3 directories, 47 files

【实例简介】

DCT域数字图像水印的MATLAB实现

论文《As-Projective-As-Possible Image Stitching with Moving DLT》中的拼接算法，对于视差图像拼接具有一定的鲁棒性，但是对特征点数量及其分布均匀性有较高的要求。 The success of commercial image stitching tools often leads to the impression that image stitching is a “solved problem”.The reality, however, is that many tools give unconvincing results when the input photos violate fairly restrictive imaging assumptions;the main two being that the photos correspond to views that differ purely by rotation, or that the imaged scene is effectively planar.Such assumptions underpin the usage of 2D projective transforms or homographies to align photos. In the hands of the casual user,such conditions are often violated, yielding misalignment artifacts or “ghosting” in the results. Accordingly, many existing imagestitching tools depend critically on post-processing routines to conceal ghosting. In this paper, we propose a novel estimationtechnique called Moving Direct Linear Transformation (Moving DLT) that is able to tweak or fine-tune the projective warp toaccommodate the deviations of the input data from the idealized conditions. This produces as-projective-as-possible image alignmentthat significantly reduces ghosting without compromising the geometric realism of perspective image stitching. Our technique thuslessens the dependency on potentially expensive postprocessing algorithms. In addition, we describe how multipleas-projective-as-possible warps can be simultaneously refined via bundle adjustment to accurately align multiple images for largepanorama creation.

【实例简介】思维进化算法应用于优化BP神经网络的初始权值和阈值

【实例简介】

MMC 载波移相MATLAB仿真

【实例简介】