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matlab中wave文件的信号生成功能源代码，欢迎使用(Matlab document which wave signals Health successful energy code, welcomed the use of)

话务分析软件，多时段统计，方便快捷，便于分析统计( This Program is Designed To Simulate The Smart Antenna System On A BTS Receiver ( Uplink ) General Constraints For Using This Software Antenna Array Of Four Elements Operating On 2 GHz With A Separation Distance 0.075 Meters Narrowband ( Uncorrelated Or Partially Correlated ) Signals Are Assumed An Authentication Code Of 10 Bits Is Sent First Two Users Are Served Only In The Presence Of Additive White Gaussian Noise Only )

ADPBS ADAPTIVE PARTIAL BUFFER SHARING QUEUE

matlab tutorial free

用matlab实现的白平衡处理算法，以亮度最大值的0.95倍为条件定义白点集合(Processing algorithms using matlab to achieve balance, to 0.95 times the maximum brightness of the white point is defined as the set of conditions)

此内容是电气工程及其自动化专业毕业设计中 所能用到得MATLAB仿真(This content is graduated in electrical engineering and automation design can be used in MATLAB simulation)

OFDM水声通信系统中的matlab程序仿真(OFDM communication system simulation)

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.)

NSGA2算法中文版详细介绍,非常实用的多目标优化代码(NSGA2 algorithm Chinese version in detail)

说明：  中文版完善的matlab simulink 电气模块设计（SimPowerSystems）使用说明，一步一步教你电气模块(Chinese version of matlab simulink comprehensive modular design Electrical (SimPowerSystems) the use of that teach you step-by-step electrical module)