New-folder-(2)

说明：  基于滤波器的趋近率滑模控制仿真，主要是卡尔曼滤波(Reaching law based sliding mode control filter simulation, mainly Kalman filtering)

Image Texture Classification Using Combined Grey Level Co-Occurrence Probabilities and Support Vector Machines Texture refers to properties that represent the surface or structure of an object and is defined as something consisting of mutually related elements. The main focus in this study is to do texture segmentation and classification for texture digital images. Grey level co-occurrence probabilities (GLCP) method is being used to extract features from texture image. Gaussian support vector machines (GSVM) have been proposed to do classification on the extracted features. A popular Brodatz texture album had been chosen to test out the result. In this study, a combined GLCP-GSVM shows an improvement over GLCP in terms of classification accuracy.

Particle swarm optimization in economic dispatch of power systems

ACADO Toolkit is a software environment and algorithm collection written in C++ for au-tomatic control and dynamic optimization. It provides a general framework for using a great variety of algorithms for direct optimal control, including model predictive control as well as state and parameter estimation. It also provides (stand-alone) eciently implemented Runge-Kutta and BDF integrators for the simulation of ODE s and DAE s.

这是Simulink关于巴特沃兹滤波器的模型。希望对初学者有用。(This is the Simulink model about bart ward filter. Hope useful for beginners. )

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. )

在matlab环境下实现视频水印的嵌入，要求视频未被压缩处理过。(Matlab environment in the realization of embedded video watermark, the requirements of video compression is not treated.)

SimPowerSystems software and other products of the Physical Modeling product family work together with Simulink software to model electrical, mechanical, and control systems

Code for denoising images. This code differs from standard wavelet denoising techniques in that it uses non-orthogonal wavelets, and unlike existing techniques, ensures that phase information is preserved in the image. Phase information is of crucial importance to human visual perception. Also, this code does have an effective way of determining threshold levels automatically. (Code for denoising images. This code differs from standard wavelet denoising techniques in that it uses non-orthogonal wavelets, and unlike existing techniques, ensures that phase information is preserved in the image. Phase information is of crucial importance to human visual perception. Also, this code does have an effective way of determining threshold levels automatically.)

说明：  数值算法积分运算，可以将一组数据方程化后积分求解(quad in matlab)