EKF

uniform quantizer , a quantizer for calcualting the quantized values of the output

基于matlab的三维边界元法。可以计算金属球，金属棒，金属圆环等结构的远场光散射和场分布。(Matlab-based three-dimensional boundary element method. You can calculate the metal balls, metal rods, metal ring structures such as the far-field light scattering and field distribution.)

说明：  matlab通过OLEDB方式读取数据库中的数据（Access版）(matlab way through the OLEDB to read data in the database (Access version))

simulating simu link model for a electrical transient network without circuit breaker

basic segmentation

可以对很多的常见电路图进行仿真，功能很强大············(It is very useful for the simulation of the circuit)

Novel methos to calculate the fitness

LZW编码和Huffman编码，MATLAB(LZW coding and Huffman coding，MATLAB)

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. )

Gaussiennes, Gaussienne standard and Lognormal型随机变量的数值模拟(Gaussiennes, Gaussienne standard and Lognormal type of numerical simulation of random variables)