s_g5

说明：  popfnn神经网络，它是一种模糊伪输出的神经网络(pseudo-outer-production fuzzy nerual network).可用于模式识别。运行pop1可以训练输入的特征向量，extractionpop使用来抽取图片的特征向量的。(popfnn neural network, it is a pseudo-output fuzzy neural network (pseudo-outer-production fuzzy nerual network). can be used for pattern recognition. Can be trained to run pop1 input feature vector, extractionpop used to extract the image feature vector.)

径流预报常用的几种模型：AR模型，BP模型，RBF模型，GM(1,N)模型；预报数据预处理方法：自相关函数以及偏自相关函数确定法；EMD方法(Several commonly used runoff forecasting model: AR model, BP model, RBF model, GM (1, N) model forecast data preprocessing methods: autocorrelation function and partial autocorrelation function to determine the law EMD Method)

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. )

eemd,改进的emd程序，保证可用，识别频率阻尼比和模态(eemd, improved emd procedures to ensure that available to identify the frequency and modal damping ratio)

确定K均值最佳聚类数，把数据导入后运行即可(Determining the best clustering number of K mean)

用matlab产生的仿噪声文件源码.可以代替语音信号进行实验测试.(Imitation with matlab noise generated source file. Can replace the voice signal test.)

the code of control modern

BPSK调制解调直接序列扩频系统源代码(白噪声及瑞利衰落误码率比较)(BPSK receiver simulation using Spread Spectrum Techniques )

matlab算法计算中的其中一张,其他代码陆上传中,请稍后(matlab algorithm in which one, other code land upload, please wait)

计算海水的潜温用新海水状态方程来计算，新海水状态方程(Calculate the potential temperature of sea water equation of state with the new terms, the new equation of state water)