SUOXIANG222

用C语言编写的模糊PID，可以用在MATLAB的仿真环境，也可以用在其他地方。(C language with the fuzzy PID, can be used in MATLAB simulation environment can also be used in other places.)

广播数据系统技术规范（中国RDS相关技术规范）(Radio Data System Specification (China RDS-related technical specifications))

关于turbo编解码的源程序 matlab仿真程序 运行成功(the program about the turbo coding and decoding and it can run succefully)

Matlab应用篇提供了大量的Matlab应用实例。配合前三篇所讲内容，巩固Matlab知识。(Matlab Application chapter provides a large number of application examples Matlab. Compatible with the three have said, the consolidation of Matlab knowledge.)

自己编写的matlab程序，采用Prewitt和Sobel算子，初学者相互学习。(I have written matlab program using Prewitt operator and Soble operator handling. Beginners learn each other.)

朴素贝叶斯分类器，朴素贝叶斯分类器朴素贝叶斯分类器(Naï ve Bayes Classification,Naï ve Bayes Classification,Naï ve Bayes Classification)

小波包分解，提取能量特征，然后进行归一化处理，获取诊断信息(Wavelet Neural Network)

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. )

public guideness for swarm Ops in Matlab

x0 = ones(2,2) Make a starting guess at the solution options = optimset( Display , off ) Turn off Display [x,Fval,exitflag] = fsolve(@myfun,x0,options) The solution is x = -0.1291 0.8602 1.2903 1.1612 Fval = 1.0e-009 * -0.1619 0.0776 0.1161 -0.0469 exitflag = 1 and the residual is close to zero. sum(sum(Fval.*Fval)) ans = 4.7915e-020