-
apply3d
Apply a function intended for one-dimensional usage independently to the three dimensionsof a 3-D array.
- 2010-06-30 20:17:34下载
- 积分：1
-
Unit-Commitment-Methods-to-Accommodate-High-Level
Unit Commitment Methods to Accommodate High Levels of Wind Generation
- 2014-01-17 15:29:01下载
- 积分：1
-
MATLAB_filter
基于MATLAB的数字滤波，包含源程序及说明(MATLAB-based digital filtering, including source and description)
- 2008-03-29 17:02:40下载
- 积分：1
-
RD
说明： 合成孔径成像雷达的经典RD算法，可以用来在某些机载雷达上使用，或用于初学者仿真(Synthetic aperture radar imaging of the classic RD algorithm can be used in some airborne radar use, or for beginners Simulation)
- 2011-03-06 12:21:30下载
- 积分：1
-
readuff
reading universal file format
- 2012-04-06 01:45:59下载
- 积分：1
-
kaerman
用卡尔曼滤波器实现目标追踪，利用蒙特卡洛的方法对跟踪滤波器进行仿真分析，次数为1000次
(Kalman filter to achieve target tracking, using the Monte Carlo method of tracking filter simulation analysis, the number is 1000 times)
- 2013-09-03 17:16:41下载
- 积分：1
-
filter-lpf
low pass filter, this program can be to use how we can see a filter is worked
- 2012-02-08 15:29:55下载
- 积分：1
-
GSO
一个基于matlab的GSO(群搜索)寻优算法例程，通过修改适应度函数可以完成不同的寻优任务，可用于开发改进算法或与其他智能优化算法进行对比。(A matlab-based GSO (group search) algorithm optimization routines, by modifying the fitness function optimization can perform different tasks that can be used to develop improved algorithms or other optimization algorithms and intelligent comparison.)
- 2016-04-13 09:57:59下载
- 积分：1
-
PC and MTD
二相编码信号的产生及脉冲压缩和MTD处理(Generation of Two - Phase Coded Signals and Pulse Compression and MTD Processing)
- 2017-07-02 15:37:42下载
- 积分：1
-
K-meanCluster
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.)
- 2007-11-15 01:49:03下载
- 积分：1
