WatermarkingDCT

matlab软件中有关elman神经网络预测的程序(matlab software procedures elman neural network prediction)

matlab数据结构 算法工具箱, 包括堆栈 队列 2叉树 红黑树 等等(Data Structures & Algorithms Toolbox ==================================== The Data Structures & Algorithms Toolbox provides advanced data structures and algorithms for the Matlab(r) 5 environment. It implements functions to create and destroy singly and doubly linked list, stack, queue, binary tree and red-black (balanced) binary tree. Data elements can be inserted, deleted or searched for. A graphical representation of trees can be shown. All algorithms are clearly written as m-files, which allows the toolbox to be used as a teaching aid in educational data structures courses. This is the first implementation available of advanced datatypes, common in other languages, for Matlab.)

matlab 版本的LDPC码的译码工具包，很实用(LDPC decoding tollkit of MATLAB Version)

采用bp神经网络对函数y=sin（x）进行训练，然后用测试集对训练的函数进行测试，也就是函数逼近sin（x)的曲线(Bp neural network using the function y = sin (x) for training, and then use the test set to test the training function, that is, function approximation sin (x) curve)

说明：  自适应算法,能够事项波束成形,比较新的代码 (adaptive algorithm)

说明：  介绍了二级倒立摆的建模，并结合极点配置方法和LQR方法进行了仿真，有详细的过程。(Introduced the double inverted pendulum modeling, combined with pole placement method and LQR method of simulation, a detailed process.)

说明：  qpsk仿真程序，模块，是一个完整的好程序 不可错过阿。(keying simulation program, module, is a complete program is not to be missed A.)

说明：  有关MATLAB边缘检测源代码，我自己的毕业设计程序，大家共同分享(MATLAB source code for the edge detection, my own graduate design program, we share)

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

Ecuacion necesaria para metodo de multiplicacion