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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. )

PURPOSE: GARCH(P,Q) parameter estimation with normal innovations using analytic derivatives

数字图像处理和机器识别C++和matlab识别书籍第三部分，讲解详尽，适合学习(Digital image processing and machine identification C++ and matlab identification books first part, explain the detailed, suitable for learning)

功能描述：测试LMS与RLS算法，比较两种算法的收敛特性 文件名：LMS_RLS_sim.m 测试用例： x(n)+a1*x(n-1)+a2*x(n-2)=e(n)，a1=-1.6,a2=0.81,e(n)为高斯白噪声 文件输出：系数a1的值 调用函数：function [A] = LMS_Algo(M,N,mu,xn) 被调用：无 作者：mingcheng 编写时间：2009-10-13 修改时间：2009-10-13 版本：V1.0 ( Function Description: Test LMS and RLS algorithm, the convergence characteristics were compared file name: LMS_RLS_sim.m test case: x (n)+ a1* x (n-1)+ a2* x (n-2) = e (n), a1 =- 1.6, a2 = 0.81, e (n) is Gaussian white noise file output: the value of coefficient a1 call the function: function [A] = LMS_Algo (M, N, mu, xn) is called: No of: mingcheng write time :2009-10-13 modified :2009-10-13 version: V1.0 )

OLSA Israel cohen speech enhancement

基于matlab的遗传算法的M文件，以前跟老师做srp项目时候找到的，很有用哦(Matlab genetic algorithm based on M-file)

matlab truss stiffness

说明：  一个简单的自适应lms算法，用matlab做的仿真，很适合初学者(A simple adaptive lms algorithm, the simulation with matlab do, it is suitable for beginners)

It is array factor using matlab.

OQAM pre processing