ANN

The earliest work on adaptive filters may be traced back to the late 1950s, during which time a number of researchers were working independently on theories and applications of such filters. From this early work, the least-mean-square ð LMSÞ algorithm emerged as a simple, yet effective, algorithm for the design of adaptive transverse (tapped-delay-line) filters.

用Matlab进行曲面的插值和拟合，来进行数值优化(Matlab surface interpolation and fitting, for numerical optimization)

PID控制器的模型仿真，结果三环的仿真正确(PID controller model simulation, the results of three-ring of the simulation correctly)

trust_region求解约束优化问题 This trust region algorithm incorporates bound constraintstogether with an ellipsoidal trust region( This trust region algorithm incorporates bound constraintstogether with an ellipsoidal trust region)

地铁火灾疏散模型 元细胞自动机 神经网络 BP 蚁群模型鱼群模型(Subway fire evacuation model meta-cellular automata neural network BP ant colony model fish colony model)

说明：  sift特征提取，然后再利用svm算法实现特征分类预测(SIFT feature extraction, and then use SVM algorithm to achieve feature classification and prediction)

三次样调插值 源程序,欢迎下载交流学习(Cubic Spline Interpolation transfer source, welcome to download the exchange of learning)

matelab实现PID功能 另附有一段与组态王通信的DDE设置(matelab PID function attached to achieve a period of DDE to communicate with the configuration settings Wang)

超导储能蓄电池混合储能在风力发电中的应用(Superconducting magnetic energy storage)

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.)