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Description: kd树的实现，用到matlab-kd tree realization used Matlab
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Size: 114688 |
Author: 请不要用公用帐号上载 |
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Description: 经验模式分解的matlab程序
hibert-huang变换，效果挺好的-empirical mode decomposition process hibert Matlab- huang transform, the effect is very good
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Size: 18432 |
Author: 姜浩 |
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Description: Cohen-Daubechies-Fauraue 9-7 Wavelet Transforms . CDF小波分解matlab源代码-Cohen-Daubechies- Fauraue 9-7 Wavelet Tr ansforms. CDF wavelet decomposition Matlab source code
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Size: 1024 |
Author: 王波 |
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Description: 插值法求出阶跃响应的Ts，Tr，deta，性能指标。方法准确，简单-interpolation step response is calculated by the Ts, Tr, deta, performance indicators. The method is accurate and simple
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Size: 1024 |
Author: sahfasf |
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Description: ual-Tree Complex Wavelet Transform Pack - version 4.3
Nick Kingsbury, Cambridge University, June 2003.
This pack (version 4.2) includes the following functions for performing
the Dual Tree Complex wavelet Transform on 1-D and 2-D signals-ual-Tree Complex Wavelet Transform Pack- Nick Kingsbury version 4.3. Cambridge University, health systems. This pack (version 4.2) includes the following functions for performing the Dual Tr ee Complex wavelet Transform on 1-D and 2-D sign als
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Size: 74752 |
Author: 王僮 |
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Description: MATLAB仿真uwb使用bpsk调制解调的发送(TX)和接收(RX)-MATLAB uwb bpsk use of modulation and demodulation of this (TX) and receive (RX)
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Size: 6144 |
Author: 陈陈 |
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Description:
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Size: 370688 |
Author: 程艳花 |
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Description: On-Line MCMC Bayesian Model Selection
This demo demonstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar -xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
-On-Line MCMC Bayesian Model Selection
This demo demonstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar-xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
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Size: 16384 |
Author: 徐剑 |
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Description: This demo nstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar -xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". 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 how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar-xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
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Size: 220160 |
Author: 晨间 |
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Description: 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.
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Size: 348160 |
Author: 晨间 |
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Description: The algorithms are coded in a way that makes it trivial to apply them to other problems. Several generic routines for resampling are provided. The derivation and details are presented in: Rudolph van der Merwe, Arnaud Doucet, Nando de Freitas and Eric Wan. The Unscented Particle Filter. Technical report CUED/F-INFENG/TR 380, Cambridge University Department of Engineering, May 2000. After downloading the file, type "tar -xf upf_demos.tar" to uncompress it. This creates the directory webalgorithm containing the required m files. Go to this directory, load matlab5 and type "demo_MC" for the demo.
-The algorithms are coded in a way that makes it trivial to apply them to other problems. Several generic routines for resampling are provided. The derivation and details are presented in: Rudolph van der Merwe, Arnaud Doucet, Nando de Freitas and Eric Wan. The Unscented Particle Filter. Technical report CUED/F-INFENG/TR 380, Cambridge University Department of Engineering, May 2000. After downloading the file, type "tar-xf upf_demos.tar" to uncompress it. This creates the directory webalgorithm containing the required m files. Go to this directory, load matlab5 and type "demo_MC" for the demo.
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Size: 58368 |
Author: 晨间 |
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Description: 这是一个较为完整的tr方式uwb接收机的M文件，里面有理论和仿真结果~是一个师兄的毕业论文程序-This is a more comprehensive way tr UWB receiver M documents, there are theoretical and simulation results ~ is a senior thesis process
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Size: 11054080 |
Author: 王野 |
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Description: SCM 信道模型的相关仿真程序：3GPP-TR-25.996-SCM channel model of the relevant simulation program: 3GPP-TR-25.996
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Size: 49152 |
Author: 陈功 |
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Description: QAM Matlab程序，误码率的坐标，星座图等在图中已经显示出来-QAM Matlab procedure, the coordinates of BER, constellation diagram, etc. At chart has been displayed
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Size: 3072 |
Author: 哈哈 |
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Description: Spatial Channel Model for MIMO Simulations. A Ray based Simulator based on 3GPP TR 25.996 v.6.1.0
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Size: 572416 |
Author: vasikara |
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Description: this 3d tr model for airplane target tracking-this is 3d tr model for airplane target tracking
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Size: 2048 |
Author: haleh |
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Description: 决策树算法的matlab实现，主要适用的是id3
算法思想-Decision Tree Algorithm to achieve the matlab main id3 algorithm is applicable to thinking
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Size: 6144 |
Author: fj |
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Description: QPSK modulation system with recover loops-This is a model of a QPSK modulation system for transmission over a bandpass channel with fc = 100 Hz and B = 30 Hz and AWGN at the receiver. SRRC fi lters with excess bandwidth α = 0.18 are employed. The receiver includes an NDA timing recovery loop followed by a DD phase recovery loop. The transmitter?s symbol period is T and set to a value of 100 ms. The symbol period at the receiver is Tr. The model will produce six windows: Two constellation plots and two eye diagrams (for the real part only), at the output of the matched fi lters and after the recovery loops a plot of the phase error (output of the NCO) and a plot of the fractional timing error (used in the interpolator). The frequency off set at the receiver is denoted by deltaf. The model simulates the transmission of 8000 QPSK symbols.
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Size: 15360 |
Author: ranbowang |
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Description: ofdm系统的将 papr算法，传统的预留子载波方法，TR算法-ofdm system will papr algorithm, the traditional method of sub-carriers reserved, TR algorithm
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Size: 2048 |
Author: ss |
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Description: 25.996信道模型的matlab代码，根据标准协议编写(channel model of 25.996)
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Size: 49152 |
Author: bucatti |
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