MPC_Exercise_Second

用Matlab仿真实现最小二乘法和总体最小二乘法估计 假设仿真的观测数据 产生，其中 为0均值， 单位方差的高斯白噪声，取n=1,2,....128。 试用TLS，取AR阶数为4，估计AR参数 和正弦波频率；再用SVD-TLS ，估计AR参数 和正弦波频率。 (1)、在仿真中，AR阶数取为4和6。 (2)、执行SVD-TCS时，AR未知。仿真运行至少二十次。 (Simulation using Matlab and the overall least squares least squares estimate assumptions simulated observation data are generated, where 0 mean, unit variance Gaussian white noise, take n = 1,2 ,.... 128. Try TLS, take AR order of 4, estimated AR parameters and the sine wave frequency then SVD-TLS, estimated AR parameters and the sine wave frequency. (1), in the simulation, AR order is taken as 4 and 6. (2), when the implementation of SVD-TCS, AR is unknown. Simulation run at least twenty times.)

说明：  matlab 代码实现控制方面，混沌线性控制，易于实现 (matlab code control, chaotic linear control, easy to implement)

function g=nonlineareq(x) Here is an example of nonlinear system that can be solved by using the Matlab command "fsolve" written in the file called "solution.m" (see quasi-Newton or Levenberg-Marquardt methods). g(1)=x(1)-4*x(1)*x(1)-x(1)*x(2) g(2)=2*x(2)-x(2)*x(2)+3*x(1)*x(2)

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matlab code for sensor nodes

OF helpers for mtd.

该源程序代码实现了如：纯ALOHA，扩展ALOHA,CSMA,ISMA等的多径通信协议通信仿真实现，程序结果给出了各个通信协议下传输数据的成功率和时间延迟。(Realize that the source code such as: pure ALOHA, the expansion of ALOHA, CSMA, ISMA and other multi-path communication protocol communication simulation realize, given the results of the proceedings of various communication protocols to transmit data under the success rate and time delay.)

病虫害分类用,用来进行病害虫分类的MATLAB程序(Pest and disease classification used, the classification used for pest MATLAB program)

This code shows you how to generate level set descriptions of various two dimensional interfaces.

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