模糊增益调度的 PID 控制器

Quantum Particle Swarm Optimization matlab

实现了数字水印和MD5值序列的嵌入以及提取，有很好的鲁棒性(MD5 realized the value of digital watermarking and embedding and extraction sequence, there is robust)

根据实测加速度振动信号，求取功率谱密度，为疲劳分析提供数据支撑(According to the measured acceleration vibration signal, the power spectrum density is obtained, providing data support for fatigue analysis.)

说明：  利用Matla编写的NSGA-II算法，用于求解车间调度的多目标问题(The NSGA-II algorithm written by Matla is used to solve the multi-objective problem of job shop scheduling.)

Multi-Scale Structural Similarity Index (MS-SSIM). Download here. Please cite the following paper in any published work if you use this software. Z. Wang, E. P. Simoncelli and A. C. Bovik, "Multi-scale structural similarity for image quality assessment," IEEE Asilomar Conference Signals, Systems and Computers, Nov. 2003.

说明：  matlab实用程序百例 图像分析 图像的区域处理 图像的颜色处置 等实例(Utility hundred cases of image analysis matlab image processing the image color region instance disposal)

利用matlab写成的窄带噪声发生，很简单实用，做毕设的时候自己琢磨的，希望对大家有帮助哈(Written by narrow-band noise matlab place, very simple and practical to do when the complete set on her own, and we hope to help Kazakhstan)

fft变换举例，实例分析MATLAB 中的FFT变换的使用方法，清晰明了。(fft transform example)

可逆跳跃马尔科夫蒙特卡洛贝叶斯模型选择，主要用于神经网络(Reversible Jump MCMC Bayesian Model Selection This demo demonstrates 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.)

Hi friends.I m doing my project in Fuzzy logic image compression please help and guide me.