偽隨機(jī)數(shù)生成器，Implementation of the Quasi-Random Number generator currently hardwired to no more than 52 dimensions
上傳時(shí)間： 2013-12-20
上傳用戶：teddysha
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.
標(biāo)簽： reversible algorithm the nstrates
上傳時(shí)間： 2014-01-08
上傳用戶：cuibaigao
Obtain the CDF plots of PAPR from an OFDM BPSK transmission specified per IEEE 802.11a specification Per the IEEE 802.11a specifications, we 52 have used subcarriers. Given so, the theoretical maximum expected PAPR is 52 (around 17dB). However, thanks to the scrambler, all the subcarriers in an OFDM symbol being equally modulated is unlikely. Using a small script, the cumulative distribution of PAPR from each OFDM symbol, modulated by a random BPSK signal is obtained
標(biāo)簽： specification transmission specified Obtain
上傳時(shí)間： 2016-04-24
上傳用戶：haoxiyizhong
This folder has some scritps that you may find usefull. All of it comes from questions that I ve received in my email. If you have a new request/question, feel free to send it to marceloperlin@gmail.com. But please, don t ask me to do your homework. Passing_your_param0 This folder contains instructions (and m files) for passing you own initial parameters to the fitting function. I also included a simple simulation script that will create random initial coefficients (under the proper bounds) and fit the model to the data.
標(biāo)簽： that questions scritps usefull
上傳時(shí)間： 2013-12-28
上傳用戶：netwolf
This LDPC software is intended as an introduction to LDPC codes computer based simulation. The pseudo-random irregular low density parity check matrix is based on Radford M. Neal’s programs collection, which can be found in [1]. While Neal’s collection is well documented, in my opinion, C source codes are still overwhelming, especially if you are not knowledgeable in C language. My software is written for MATLAB, which is more readable than C. You may also want to refer to another MATLAB based LDPC source codes in [2], which has different flavor of code-writing style (in fact Arun has error in his log-likelihood decoder).
標(biāo)簽： LDPC introduction simulation software
上傳時(shí)間： 2014-01-14
上傳用戶：大融融rr
自己編的一個(gè)用隨機(jī)行走法實(shí)現(xiàn)電路計(jì)算的例子，電路圖及參數(shù)均在Partial random walk for large linear network analysis里
上傳時(shí)間： 2016-05-19
上傳用戶：teddysha
This LDPC software is intended as an introduction to LDPC codes computer based simulation. The pseudo-random irregular low density parity check matrix is based on Radford M. Neal’s programs collection, which can be found in [1]. While Neal’s collection is well documented, in my opinion, C source codes are still overwhelming, especially if you are not knowledgeable in C language. My software is written for MATLAB, which is more readable than C. You may also want to refer to another MATLAB based LDPC source codes in [2], which has different flavor of code-writing style (in fact Arun has error in his log-likelihood decoder).
標(biāo)簽： LDPC introduction simulation software
上傳時(shí)間： 2014-12-05
上傳用戶：change0329
Numerical Computing with MATLAB （by Cleve Moler) is a textbook for an introductory course in numerical methods, Matlab, and technical computing. The emphasis is on in- formed use of mathematical software. We want you learn enough about the mathe- matical functions in Matlab that you will be able to use them correctly, appreciate their limitations, and modify them when necessary to suit your own needs. The topics include * introduction to Matlab, * linear equations, * interpolation, * zero and roots, * least squares, * quadrature, * ordinary di?erential equations, * random numbers, * Fourier analysis, * eigenvalues and singular values, * partial di?erential equations.
標(biāo)簽： introductory Numerical Computing textbook
上傳時(shí)間： 2016-07-04
上傳用戶：思琦琦
Toolbox for Numerical Computing with MATLAB （by Cleve Moler). Numerical Computing with MATLAB （by Cleve Moler) is a textbook for an introductory course in numerical methods, Matlab, and technical computing. The emphasis is on in- formed use of mathematical software. We want you learn enough about the mathe- matical functions in Matlab that you will be able to use them correctly, appreciate their limitations, and modify them when necessary to suit your own needs. The topics include * introduction to Matlab, * linear equations, * interpolation, * zero and roots, * least squares, * quadrature, * ordinary di?erential equations, * random numbers, * Fourier analysis, * eigenvalues and singular values, * partial differential equations.
標(biāo)簽： Numerical Computing MATLAB with
上傳時(shí)間： 2014-01-01
上傳用戶：guanliya
Digital Signal and Image Processing Using MATLAB The most important theoretical aspects of image and signal processing (ISP) for both deterministic and random signals are covered in this guide to using MATLAB® . The discussion is also supported by exercises and computer simulations relating to real applications such as speech processing and fetal-heart–rhythm tracking, and more than 200 programs and functions for numerical experiments are provided with commentary.
標(biāo)簽： theoretical Processing important Digital
上傳時(shí)間： 2016-07-08
上傳用戶：z1191176801
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