freecontrol1

说明：  ml detection mimo good

说明：  离散信号处理举例，开发环境是MATLAB。(Discrete signal processing, for example, development environment is MATLAB.)

噪声，《计算机图像处理与识别技术》源代码 (noise in matlab)

Bp神经网络实现语音信号的分类 效果十分好 值得参考(Language signal classification Bp neural network)

EMD工具包 HT,HHT变换最新的工具(EMD kit HT, HHT transform the latest tools)

MOEAs对于度欧表的有 花纹那(MOEAs for the degree of European tables have patterns that)

MATLAB计算机视觉与深度学习实战-运行视频(As a biological technology, face is difficult to be stolen and infection-free. In addition to that, it enjoys the authentication concealment and is never forgotten. Therefore, authentication based on face recognition will become the mainstream of future development. It has important and extensive application in various fields, and is also a hot topic at present biological characteristics identification technology research. Under the non-restrictive condition, the face recognition rate is not high, and its accuracy needs to be further improved. In this paper, the following aspects is analyzed)

说明：  基于模糊神经网络的仿真历程，以及案例实现(Motor model, program simulation implementation, Simulink simulation based on S-function module, MATLAB simulation environment)

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. )

通过大量的撕裂介绍了如何使用MATLAB实现对电子线路、数字电路、数字信号处理、数字通信的仿真及其方法和技巧。(Through a lot of tearing describes how to use MATLAB to achieve the electronic circuits, digital circuits, digital signal processing, digital communication and its methods and techniques of simulation.)