economic-load-dispatch-problem

This package implements a well-known PCA-based face recognition method, which is called Eigenface

THRESHOLD gui FOR DEFINING THE THRESHOLD

matlab编写的串口调试助手，matlab6.5下运行(write matlab serial debugging assistant, matlab6.5 run)

Newton-Raphson root finding - single function

DCT Matlab Source Code

economic load dispach

光伏微网逆变器并网matlab/simulink仿真，仿真效果很好，mppt环节采用扰动跟踪法，锁相环部分使用s-function编译(Solar Micro Inverter Grid matlab/simulink simulation, the simulation results very well, mppt link tracking using perturbation method, phase lock loop section compiled using s-function)

LMD分解，通过局部均值分解重构信号，实现对信号的降噪效果(LMD the decomposition reconstructed signal by local means, to achieve the effect of noise on the signal)

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. )

硬件工程师必读攻略，提供了如何通过仿真有效提高数模混合设计性的有效建议(Hardware engineers must-read Raiders, provides a simulation of how to effectively improve the digital-analog hybrid design of effective proposals)