<pre id="yu4we"></pre> This folder contains all the codes based on Matlab Language for the book <《Iterative Methods for Linear and Nonlinear Equations》, and there are totally 21 M files, which can solve most of linear and nonlinear equations problems.
標(biāo)簽： Iterative the for Language
上傳時(shí)間： 2013-12-23
上傳用戶(hù)：cazjing
it contains many classic Test Problems for Unconstrained Optimization such as camel6,treccani,goldstein,branin, shubert1,Ackley,dejong,dejong1,dejong2, dpower,rastrigin,Griewangk,Schwefel, rosenbrock2 and step. the package have the contour and mesh figures of these problem. it also give m files of these problems,and you can easily get your view of figures.
標(biāo)簽： Unconstrained Optimization contains Problems
上傳時(shí)間： 2015-09-17
上傳用戶(hù)：bcjtao
％ Atomizer Main Directory, Version .802 里面信號(hào)含有分解去噪合成過(guò)程的代碼 ％--------------------------------------- -------------------------------- ％ This is the main directory of the Atomizer package the full package ％ contains over 100 files, consisting of .m files, .mex files, and datasets.
標(biāo)簽： Directory Atomizer Version Main
上傳時(shí)間： 2015-12-12
上傳用戶(hù)：youke111
CheckMate is a MATLAB-based tool for modeling, simulating and investigating properties of hybrid dynamic systems. Hybrid systems are modeled using the Simulink graphical user interface (GUI). Parameters and specifications are entered using both the Simulink GUI and user-defined m-Files. CheckMate commands are entered in the MATLAB command window.
標(biāo)簽： investigating MATLAB-based simulating properties
上傳時(shí)間： 2013-12-13
上傳用戶(hù)：源弋弋
This file contains the material presented as the first Embedded MATLAB webinar on the MathWorks web site on September 13, 2007. It contains the PDF version of presentation slides and all necessary demonstration files (including MATLAB m-Files and Simulink models).
標(biāo)簽： the MathWorks presented Embedded
上傳時(shí)間： 2016-03-07
上傳用戶(hù)：66666
n this demo, we show how to use Rao-Blackwellised particle filtering to exploit the conditional independence structure of a simple DBN. The derivation and details are presented in A Simple Tutorial on Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks. This detailed discussion of the ABC network should complement the UAI2000 paper by Arnaud Doucet, Nando de Freitas, Kevin Murphy and Stuart Russell. After downloading the file, type "tar -xf demorbpfdbn.tar" to uncompress it. This creates the directory webalgorithm containing the required m files. Go to this directory, load matlab5 and type "dbnrbpf" for the demo.
標(biāo)簽： Rao-Blackwellised conditional filtering particle
上傳時(shí)間： 2013-12-17
上傳用戶(hù)：zhaiyanzhong
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.
標(biāo)簽： demonstrates sequential Selection Bayesian
上傳時(shí)間： 2016-04-07
上傳用戶(hù)：lindor
The software implements particle filtering and Rao Blackwellised particle filtering for conditionally Gaussian Models. The RB algorithm can be interpreted as an efficient stochastic mixture of Kalman filters. The software also includes efficient state-of-the-art resampling routines. These are generic and suitable for any application. For details, please refer to Rao-Blackwellised Particle Filtering for Fault Diagnosis and On Sequential Simulation-Based Methods for Bayesian Filtering After downloading the file, type "tar -xf demo_rbpf_gauss.tar" to uncompress it. This creates the directory webalgorithm containing the required m files. Go to this directory, load matlab and run the demo.
標(biāo)簽： filtering particle Blackwellised conditionall
上傳時(shí)間： 2014-12-05
上傳用戶(hù)：410805624
In this demo, we show how to use Rao-Blackwellised particle filtering to exploit the conditional independence structure of a simple DBN. The derivation and details are presented in A Simple Tutorial on Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks. This detailed discussion of the ABC network should complement the UAI2000 paper by Arnaud Doucet, Nando de Freitas, Kevin Murphy and Stuart Russell. After downloading the file, type "tar -xf demorbpfdbn.tar" to uncompress it. This creates the directory webalgorithm containing the required m files. Go to this directory, load matlab5 and type "dbnrbpf" for the demo.
標(biāo)簽： Rao-Blackwellised conditional filtering particle
上傳時(shí)間： 2013-12-14
上傳用戶(hù)：小儒尼尼奧
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.
標(biāo)簽： sequential reversible algorithm nstrates
上傳時(shí)間： 2014-01-18
上傳用戶(hù)：康郎
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