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fdtd3D
fdtd3D simulation by matlab
- 2011-09-02 21:03:40下载
- 积分：1
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runge_kutta
本文用龙格库塔法求解了不拉休斯解。龙格库塔法是求解高阶微分方程的有力工具，本文对龙格库塔方法作了简要介绍，并附上了matlab源程序。(in this paper a runge_kutta method was used to slove the blasius equation in the environment of matlab.)
- 2009-05-02 09:15:06下载
- 积分：1
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815889771HVS
这个代码描述了人类视觉系统的几个特性，希望能够对大家有帮助，共同进步！(This code described several characteristics of human visual system, the hope can to help, make progress together!)
- 2010-11-09 20:12:25下载
- 积分：1
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YUYINXINHAO
MATLAB编程实现语音信号的时域特征提取，如分帧、过零率、短时能量、语谱图等(Speech signal feature extraction)
- 2021-04-18 14:48:52下载
- 积分：1
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Chuong-1-Ly-thuyet-thong-tin
COmmunication Information Chap I
- 2014-10-03 10:29:43下载
- 积分：1
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chazhi
matlab插值问题，内有两个简单程序和PPT详细讲解，供参考。(matlab interpolation problem, there are two simple procedures and PPT explain in detail, for reference.)
- 2013-04-26 14:51:15下载
- 积分：1
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pcaPlda
使用matlab自己编写的pca+lda方法，大家一起分享(pca and lda in matlab by myself,we will share together)
- 2013-05-22 17:59:49下载
- 积分：1
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signal
单频双频信号的检测与估计，使用LMS算法(Detection and estimation of single frequency dual frequency signals using LMS algorithm)
- 2016-03-22 18:44:43下载
- 积分：1
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fuzzy c-means
说明： 基于fuzzy c-means（FCM）的无监督模糊聚类算法，输出值有：各个样本的类别标签、目标函数在每次迭代后的值、聚类中心以及聚类区间。内有测试数据data.mat，点击 test.m 可以完美运行。(The unsupervised fuzzy clustering algorithm based on fuzzy c-means (FCM) outputs the class labels of each sample, the value of the target function after each iteration, the clustering center and the clustering interval. There is test data ('data.mat') inside, click ' test.m' to be able to run perfectly.)
- 2017-09-18 16:02:28下载
- 积分：1
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zhaoxiaopu
位置指令为幅值为1.0的阶跃信号，r（k）=1.0。网络结构为1-4-1，高斯函数的参数值取Cj=[-2 -1 1 2]T ，B=[0.5 0.5 0.5 0.5]T 。
网络权值学习参数为η=0.30，α=0.05 。PID控制各参数的初RBF网络控制，被控对象为G（s）=
取采样时间为1ms，采用Z变换进行离散化，离散化后的被控对象为
y(k)=-den(2)*y（k-1）-den(3)*y(k-2)+num(2)*u(k-1)+num(3)*u(k-2)
始值为，kp=20， kd=0.3， ki=0.1。
(Position command for the step signal amplitude of 1.0, r (k) = 1.0. The network structure is 1-4-1, the Gaussian function parameters taken Cj = [-2-1 1 2] T, B = [0.5 0.5 0.5 0.5] T. Weight learning parameter η = 0.30, α = 0.05. PID control parameters at the beginning of each RBF network control, the controlled object is G (s) = take the sampling time is 1ms, using the Z transform discrete, discretized controlled object is y (k) =-den (2)* y (k-1)-den (3)* y (k-2)+num (2)* u (k-1)+num (3)* u (k-2) initial value, kp = 20, kd = 0.3, ki = 0.1.)
- 2013-07-22 21:20:28下载
- 积分：1
