-
LFM-MP-SNR
信号与信息处理——阵列信号处理DOA估计的matlab算法，这是线性调频信号的稀疏分解算法，wigner_will分布(Signal and information processing- array signal processing matlab algorithm for DOA estimation, which is a linear FM signal sparse decomposition algorithm, wigner_will distribution)
- 2007-09-04 10:47:39下载
- 积分：1
-
dscdma
DS-CDMA 通信系统的仿真，其中的信道为瑞丽衰落信道(Simulation program to realize DS-CDMA system)
- 2013-01-14 14:35:31下载
- 积分：1
-
invertedpendulum
M file for Inverted pendulum
- 2007-12-10 10:54:15下载
- 积分：1
-
picdata
冈萨雷斯书籍图像库
图像的基本
一些李娜图像等等(Gonzalez basic image image library books)
- 2013-12-04 21:20:28下载
- 积分：1
-
guanliandufnex
基于灰色预测模型对旅游人数的预测matlab代码(Grey prediction model based on the forecast of tourist arrivals matlab code)
- 2011-02-05 21:53:49下载
- 积分：1
-
fusion
说明： 电子科技大学的一篇关于多传感器融合的好文章,请站长审阅(University of Electronic Science and Technology of multi-sensor fusion on a good article, please review head)
- 2009-07-31 09:14:30下载
- 积分：1
-
LMSfilter
假设一个接收到的信号为：x(t)=s(t)+n(t), 其中s(t)=A*cos(wt+a), 已知信号的频率w=1KHz，
而信号的幅度和相位未知，n(t)是一个服从N(0,1)分布的白噪声。为了利用计算机对信号进行处理，
将信号按10KHz的频率进行采样。
通过对x(t)进行LMS自适应信号处理，从接收信号中滤出有用信号s(t).
在未知信号频率的情况下，通过对x(t)进行LMS自适应信号处理，从接收信号中滤出有用信号s(t).
(Assuming a received signal: x (t) = s (t)+ n (t), one of s (t) = A* cos (wt+ a), the known frequency signal w = 1KHz,
And the signal amplitude and phase of the unknown, n (t) is a subject to N (0,1) the distribution of white noise. The use of computers in order to deal with the signal,
The signal by sampling frequency of 10KHz.
Of x (t) for LMS adaptive signal processing, filtering from the received signal in the useful signal s (t).
Frequency signals in unknown circumstances, the adoption of x (t) for LMS adaptive signal processing, filtering from the received signal in the useful signal s (t).)
- 2009-03-31 18:17:09下载
- 积分：1
-
nc_tanker
Neural control (reinforcement learning) for tanker heading
- 2010-01-12 02:47:07下载
- 积分：1
-
logisticmap
bifurcation and lyapunov analysis of logistic map using matlab
- 2013-03-19 04:43:38下载
- 积分：1
-
base-GSO
自己调试的GSO程序，简单可运行，适合初学者参考。(The glowworm swarm optimization (GSO) is a swarm intelligence optimization algorithm developed based on the behaviour of glowworms (also known as fireflies or lightning bugs). The behaviour pattern of glowworms which is used for this algorithm is the apparent capability of the glowworms to change the intensity of the luciferin emission and thus appear to glow at different intensities.)
- 2013-12-17 11:15:33下载
- 积分：1
