draw_roc

Software package for fuzzy calculus

用遗传算法解决最短路径优化TSP问题(附matlab源程序.txt)(The solvelution of the shortest path optimization problem TSP by using genetic algorithms)

有限时域差分算法Matlab程序，用于计算场强分布。(finite time domain finite difference algorithm is used to calculate the field intensity distribution)

说明：  求解送取货且带时间窗的绿色车辆路径与调度优化问题(Solving the green vehicle routing and scheduling optimization problem with time window)

升降压DC——DC变换器的simulink模型，前相为buck变换器，后相为boost变换器，可以进行适当的升降电压调节。(The buck-boost DC- DC converter simulink model, phase buck converter, the latter phase of the boost converter can be appropriate lifting voltage regulator.)

matlab code for speech recognition of single utterances

读入语音信号，加入噪声后将其滤除再还原声音(Adding a pulse noise to an existed speech then filt it with a specified FIR filter.)

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. )

it is about cognitive radio

motor dc control whith matlab