ParticleMethods_Compare

ofdm粗频偏估计程序，包含5个模块，基于PN序列的同步(ofdm coarse frequency offset estimation procedures, including the five modules)

基于神经计算的增量式PID控制器学习算法。被控对象为非线性控制系统。(keyword：PID controller 、 neural network、MATLAB )

Fuzzy聚类工具箱，含4种评价方式，内有文档及注释(FuzzyClusteringToolbox)

说明：  可以通过输入刚度或阻尼数组，生成刚度（阻尼）矩阵。(The stiffness (damping) matrix can be generated by input stiffness or damping arrays.)

说明：  分别采用欧拉法、改进欧拉法和隐式梯形法解一阶非线性微分方程dx/dt=ax。(The Euler method, the improved Euler method and the implicit trapezoidal method are used to solve the first-order nonlinear differential equation dx/dt=ax respectively.)

这是“燃气-蒸汽”联合循环机组的发电机控制器模型(This is- the generator controller model " gas-steam" combined cycle units)

将toeplitz矩阵作为压缩感知测量矩阵，并对其进行性能评估。(Toeplitz matrix as the measurement of compressed sensing matrix, and its performance evaluation.)

LSB used in watermarking for embedding

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. )

iRobotCreateSimulatorToolbox1_3 iRobot仿真matlab工具箱(iRobotCreateSimulatorToolbox1_3 iRobot simulation matlab toolbox)