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simotheor
Simo traces the ser for a simo system :n transmit antenna and 1 recieve antenna and compare it to an alamouti system
- 2009-02-26 01:14:15下载
- 积分：1
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fastica
用快速独立分量实现盲信号分离的源码程序，已经编译过的，可用(Fast independent component to realize blind signal separation
)
- 2012-04-17 12:43:22下载
- 积分：1
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MATLAB-Numerical-Evaluation
Matlab 数值计算讲义和例程，包括如下章节：
范数、条件数和方程解的精度
矩阵特征值和矩阵函数
奇异值分解
函数的数值导数和切平面
函数极值点
数值积分
随机数据的统计描述
多项式拟合和非线性最小二乘
插值和样条
Fourier分析
常微分方程
稀疏矩阵(Matlab numerical evaluation)
- 2012-05-08 15:41:22下载
- 积分：1
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4PSK
This is code to simulation of AM, FM, PSK, QAM modulation
- 2014-11-29 12:24:05下载
- 积分：1
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PCA
实现基于PCA变换的故障信号检测，可以区分正常信号和故障信号。(Realize the fault signal detection based on PCA transform, can distinguish between normal signal and fault signal.)
- 2015-02-19 12:12:36下载
- 积分：1
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yiqun
蚁群算法的程序，自己写的，用得着的可以看看(yiqun IS written by me)
- 2009-03-07 10:26:30下载
- 积分：1
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Communication_Documents
Contains OFDM Documents with all descriptions
- 2010-10-23 00:19:52下载
- 积分：1
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seizmo_mapping_features
export figure : to export figure from matlab easily and efficiently
- 2013-08-09 19:35:42下载
- 积分：1
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estimation原
说明： 能够计算IEEE14节点和IEEE30节点的状态估计(It can calculate the state estimation of IEEE14 node and IEEE30 node)
- 2021-01-24 16:14:20下载
- 积分：1
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Reversible_Jump_MCMC_Bayesian_Model_Selection
This demo nstrates the use of the reversible jump MCMC algorithm for neural networks. It uses a hierarchical full Bayesian model for neural networks. This model treats the model dimension (number of neurons), model parameters, regularisation parameters and noise parameters as random variables that need to be estimated. The derivations and proof of geometric convergence are presented, in detail, in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Robust Full Bayesian Learning for Neural Networks. Technical report CUED/F-INFENG/TR 343, Cambridge University Department of Engineering, May 1999. After downloading the file, type "tar -xf rjMCMC.tar" to uncompress it. This creates the directory rjMCMC containing the required m files. Go to this directory, load matlab5 and type "rjdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
(This demo nstrates the use of the reversible jump MCMC algorithm for neural networks. It uses a hierarchical full Bayesian model for neural networks. This model treats the model dimension (number of neurons), model parameters, regularisation parameters and noise parameters as random variables that need to be estimated. The derivations and proof of geometric convergence are presented, in detail, in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Robust Full Bayesian Learning for Neural Networks. Technical report CUED/F-INFENG/TR 343, Cambridge University Department of Engineering, May 1999. After downloading the file, type "tar-xf rjMCMC.tar" to uncompress it. This creates the directory rjMCMC containing the required m files. Go to this directory, load matlab5 and type "rjdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
)
- 2008-03-07 23:23:12下载
- 积分：1
