wurane2e

基于贝叶斯网络的源程序，可用于对数据的分类工作，效果较好(Based on Bayesian network source, the data can be used for the classification of work, better)

基于最小均方误差的最小二乘滤波器的设计算法(Based on the minimum mean square error of the least squares filter design algorithms)

绘图典型程序 包括简单的二维 三维 六维(Typical drawing program includes a simple two-dimensional three-dimensional six-dimensional)

说明：  这是一个LMBP算法的源程序MATLAB(THIS IS A LMBP PROGRAM)

该 程序是将经验模态函数分解为特征模态函数的一种有效方法(emd is emperical module decomposition it can decompose the signal into characteristic signals and it is an effective method.)

Distributed Localization in Wireless Ad Hoc

包含光伏电池模块、MPPT模块、BOOST模块、逆变模块，是一种双隐层反向传播神经网络，可以广泛的应用于数据预测及数据分析。( PV modules contain, MPPT module, BOOST module, inverter module, Is a two hidden layer back propagation neural network, Can be widely used in data analysis and forecast data.)

用于Matlab的hispice仿真程序， 图形界面(Hispice for the Matlab simulation program, a graphical interface)

应用一般最小二乘法与递推式最小二乘法对二阶系统进行参数辨识(RLS)

Abstract—Demand Response (DR) and Time-of-Use (TOU) pricing refer to programs which offer incentives to customers who curtail their energy use during times of peak demand. In this paper, we propose an integrated solution to predict and re-engineer the electricity demand (e.g., peak load reduction and shift) in a locality at a given day/time. The system presented in this paper expands DR to residential loads by dynamically scheduling and controlling appliances in each dwelling unit. A decision-support system is developed to forecast electricity demand in the home and enable the user to save energy by recommending optimal run time schedules for appliances, given user constraints and TOU pricing the utility company. The schedule is communicated to the smart appliances over a self-organizing home energy network and d by the appliance control interfaces developed in this