资源列表
tezhengzhi
- 利用matlab进行时域分析，本实验首录了三个音频。并编写程序求其特征值。特征值有平均值，整流平均值方差，标准差，方差，峭度，均方根，波形因子，峭度因子，峰值因子，脉冲因子，裕度因子。还有就是对音频进行自相关分析得出其波形图。-Use matlab time-domain analysis, the first experiment recorded three audio. Programming and seeking the eigenvalues. Characteristic valu
vb
- 二次规划算法可在SQP算法中成为通用的二次规划子问题程序。二次规划算法设计到众多的矩阵运算，用C++代码编程需要较大工作量，而矩阵运算在Matlab下则相当方便。-Quadratic programming algorithm can be a generic quadratic programming subproblem program SQP algorithm. Quadratic programming algorithm design to a large number of mat
vbdll
- 作为一种简单易用的Windows开发环境，Visual Basic从一推出就受到了广大编程人员的欢迎。它使 程序员不必再直接面对纷繁复杂的Windows消息，而可以将精力主要集中在程序功能的实现上，大大提高了编程效率。但凡事有利必有弊。-As an easy-to-use Windows development environment, Visual Basic a launch has been welcomed by the majority of programmers. It allo
knn
- KNN简单分类算法C++的实现，大家可以参考下，采用欧式距离，里面包含了knn.cpp以及train.txt，test.txt，rusult.txt-Classification algorithm (KNN), the realization of the c++, you can reference, USES the Euclidean distance, which contains the KNN, CPP and train. TXT, test. TXT, rusult. TXT
LEVINSON[1]
- LEVINSON-DURBIN算法的应用：基于Y-L公式的一个实验，AR模型，内含有levinson-durbin实现的matlab源码-LEVINSON-DURBIN algorithm applications: an experimental YL-based formula, AR model, containing levinson-durbin realize matlab source
KSVD
- 更简洁实现ksvd代码，比一般的代码更方便和容易理解-More concise achieve k svd code is more convenient than the general code and easy to understand
optimization
- 最优化问题的数学解析，主要讲解背包问题，最优化，最短路径等优化模型，一数学建模的方法去理解最优化问题，课后有matlab的练习以帮助理解-Optimization of mathematical analysis, mainly on the knapsack problem, optimization, etc. shortest path optimization model, a mathematical modeling approach to understand the optimi
pmacControl
- matlab中直线电机的仿真模型，使用Pmac控制卡-Simulation model in matlab linear motor, control card using Pmac
svm
- svm线性分类器-svm linear classifier
svm02
- 支持向量机(Support Vector Machine) 5步法matlab实现过程-Support vector machine (Support Vector Machine) 5-step process to achieve matlab
linear-regression
- linear regression 线性规划 matlab 应用实例-linear regression matlab
PG_DEEP-master
- A fast learning algorithm for deep belief nets 文章代码-2006 A fast learning algorithm for deep belief nets