jiaomi

本程序可以实现具有较好相关性的ZC序列，其时频两域相关性很强(The program can achieve better correlation with the ZC sequence, time and frequency domain strong correlation)

实现非负矩阵分解的程序，适合提取非负信号的特征值(A program for non negative matrix factorization, Suitable for extracting the eigenvalues of non negative signals)

本文介绍在Matlab 环境下发电厂水位控制BP 模型的建模与仿真方法, 得出了预测模型的最优参数。仿真结果表明, MATLAB 神经网络工具箱可有效地用来解决复杂的非线性控制系统的优化设计问题。(This article describes the Matlab environment plant level control BP Model modeling and simulation methods, predictive models obtained optimal parameters. Simulation results show that, MATLAB Neural Network Toolbox can be effectively used to solve complex nonlinear control system optimization design problems.)

Analyze performance of 3D FDFD algorithm for large systems.

简单的用Matlab实现关联规则基本算法Apriori算法，打开Matlab 目录调至压缩包解压的目录下，直接在matlab上运行test，有测试数据。(Simple association rules using Matlab basic algorithm Apriori algorithm, open Matlab directory raised unpack the archive directory, directly run in matlab test, with test data.)

小波神经网络,可以用于对日径流进行预测，有助于了解小波分析与神经网络结合。(Wavelet neural networks can be used to predict daily runoff, help to understand wavelet analysis and neural networks.)

EM 算法是Dempster，Laind，Rubin于1977年提出的求参数极大似然估计的一种方法，它可以从非完整数据集中对参数进行 MLE估计，是一种非常简单实用的学习算法。这种方法可以广泛地应用于处理缺损数据、截尾数据以及带有噪声等所谓的不完全数据，可以具体来说，我们可以利用EM算法来填充样本中的缺失数据、发现隐藏变量的值、估计HMM中的参数、估计有限混合分布中的参数以及可以进行无监督聚类等等。(EM algorithm is a method for maximum likelihood estimation of parameters proposed by Dempster, Laind and Rubin in 1977. It can estimate parameters from incomplete data sets by MLE. It is a very simple and practical learning algorithm. This method can be widely used in dealing with the so-called incomplete data such as defective data, censored data and noisy data. Specifically, we can use EM algorithm to fill missing data in samples, find hidden variable values, estimate parameters in HMM, estimate parameters in finite mixed distribution, and so on. Unsupervised clustering and so on.)

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)

Vibration Control of Active Structures

spike功率谱分析，对于胞外记录的实验人员有用(spike power spectrum analysis, for extracellular recording useful to experiment)