optimal

基于云模型雾化算法的实现。该程序实现了雾化算法在全局范围内的最优搜索（二维，十维，三十维）。(Aerosol cloud model based algorithm. The process of atomization Algorithm for the Global Optimum Search in the context of (two-dimensional, 10 dimensional, 30 dimensional).)

MATLAB程序，可以计算稳态噪声的响度、尖锐度等。(Calculating the loudness, sharpness of steady noise)

model predictive control using matlab

mfcc特征提取的程序,程序还需要enframe和melbankm，会一起上传(mfcc feature extraction procedures, the program also needs enframe and melbankm, upload them together)

unitary esprit 测向 direction finding (unitary esprit direction of arrival)

How the K-mean Cluster work Step 1. Begin with a decision the value of k = number of clusters Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following: Take the first k training sample as single-element clusters Assign each of the remaining (N-k) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster. Step 3 . Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample. Step 4 . Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments. (How the K-mean Cluster workStep 1. Begin with a decision the value of k = number of clusters Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following: Take the first k training sample as single-element clusters Assign each of the remaining (Nk) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster. Step 3. Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample. Step 4. Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments.)

MATLAB神经网络30个案例分析—案例29 kohonen网络的聚类算法—网络入侵聚类源代码及数据(MATLAB Neural Network Analysis of 30 cases- case the 29 kohonen network clustering algorithm- network intrusion clustering source code...)

Its a matlab code for tracking moving objects

基于matlab的SOFC的建模,用simulink的自带模块进行建模(modeling of SOFC)

代码是MATLAB环境下基于k-nn算法的分类器对数据iris的分析(under matlab environment based on k-nn classifier for the iris.data)