资源列表
FuzzyClusteringToolbox
- 一个模糊聚类工具箱，里面有四种模糊聚类算法，带有演示-four fuzzy clustering algorithms with demo in it.
FuzzyClusteringToolbox[1]
- 各种聚类方法的matlab源代码，非常全-A variety of clustering methods matlab source code, a very wide
pichuka
- control of drives with control system
FuzzyClusteringToolbox
- 四种聚类算法源代码及示例代码,本程序的最终目的是形成一套标准的用于聚类、可扩展的工具。包括的内容有1. 聚类算法：Kmeans和Kmedoid算法、FCMclust, GKclust, GGclust算法 2. 评估分类原型：程序可以在二维图像上绘制出聚类的结果 3. 验证：程序给每一个算法提供验证机制，每个聚类算法会统计Partition Coefficient (PC), Classification Entropy (CE), Partition Index (SC), Separatio
Matlabsignalprocess
- 经典数字信号处理滤波器的源代码 重点是利用巴特沃斯模拟滤波器转而设计其它数字滤波器 -Classical digital signal processing filter source code
vad
- 关于端点检测的几种方法，语音样本是自己录制的，对传统算法做了一些改进，加入了去噪，去噪之后再进行端点检测，均调通 vad0303：自己设置调整门限为一定值 vad0310：根据能量值和过零率设置门限，自适应门限值 vad0310_2：基于比例因子的门限自调整 vad0310.m加入了噪声，端点检测前都噪声进行了处理 entropy.m：基于自适应子带频谱熵的稳健性语音端点检测 可用于语音增强及端点检测 dbdoor.m：双门限算法，用于语音端点检测。可以通过调整门限值，并
Computational-Electromagnetics
- 计算电磁学，英文版，里面有些小程序，可以用来练练手，也是很基础的知识。-Computational electromagnetics, in English, which some small program that can be used to practice your hand, is a very basic knowledge.
P5_2_SampleSelection
- 样本选择实例，对数据进行衍生变量的收集和抽样，有利于训练模型-Examples of sample selection, data collection and sampling derived variables
TOOLBOX_calib.rar
- 采用M语言编写的图像处理工具箱，内容丰富，设计了WINDOWS下的界面操作，对读者十分方便,M language using the image processing toolbox, rich in content and design of the interface under WINDOWS operation is very convenient for readers
HRR-Detector-for-Slow-Moving
- 地面探测器发现海杂波中的目标一种方法 并有仿真-Based upon observations from real radar data measurements, a heuristic approach exploiting a salient aspect of the idealized LRT is developed which is shown to perform well when applied to real measured sea clutter
lectur
- image denoising and describes about the condition in which image denoising is important. Also discuss about performance measures to evaluate image denoising results. Image processing is an important step in clinical image diagnosis. Medical images ar
Wind-Turbine-System-Identification
- Currently systems and dynamics research is ongoing in modeling and identification primarily for controller design, state estimation and fault detection/diagnosis. With accurate and precise system model identification, no aspect from controls to fault