201000820134.doc

基于输出跟随的模型参考自适应控制程序，利用最小二乘进行了参数更新。(Based on the output of the model to follow reference adaptive control procedure, carried out using least squares parameter updating.)

说明：  给定n种物品和一个背包,物品i的重量是Wi,价值是vi,被包容量是C,应该如何选择装入被包的物品,使得装入背包中物品的总价值最大?对于每种物品i,只有两种选择,装入或者不装入. 输入:第一行两个正整数n(物品数)和C(容量) 第二行n个正整数n,表示物品价值 第三行n个正整数n,表示物品重量 输出:第一行表示总价值 第二行n个数(0/1),0表示物品不装入,1表示装入 例如, 输入: 5 10 6 3 5 4 6 2 2 6 5 4 输出 15 1 1 0 0 1 (Species of a given n items and a backpack, the weight of item i is Wi, the value of vi, was packet capacity is C, should be how to choose a package of items to be loaded, the backpack load in the largest total value of goods? For each item i, there are only two options, loaded or not loaded. Input: the first line of the two positive integer n (the number of items) and C (capacity) of the second line of n positive integer n, that the value of the third line of n positive integer n, that the weight of output: the total value of the first line of the second line, said n number of (0/1), 0 that the items do not load, that load 1 for example, enter: 5,106,354,622 6 5 4 output 15 1 1 0 0 1)

家庭能量管理负荷优化调度。计及光伏出力、储能对可平移负荷进行调度以实现节能效果。有注释。(Home energy management. Load optimization scheduling. PV power generation and energy storage are considered to schedule the translational load to achieve energy saving. The program has a lot of comments.)

伽辽金最小二乘无网格法、伽辽金配点无网格法等(伽辽金最小二乘无网格法、伽辽金配点无网格法等，其中许多内容都是作者课题组在国家自然科学基金资助下取得的成果。书中给出了作者用C++研制的面向对象的无网格法程序OMLL，另外各章都给出了相应的MATLAB 程序，以帮助读者理解各种无网格方法的程序实现过程。)

hinfini test under matlab

经典单相交交变换电路仿真，基于matlab7.1(Classic single-phase AC conversion circuit simulation, based on matlab7.1)

盲信号处理中的经典算法FASTICA算法，实现信号的忙源分离(Blind signal processing algorithms in the classical algorithm FASTICA achieve busy signal source separation)

matlab windup的应用文件说明,(matlab windup)

说明：  全波频率相速度分析，可以分析软硬地层，不同层数的底层(Full-wave frequency phase velocity analysis, to analyze hard and soft strata, different layers of the underlying)

yalmip最新文档，可在matlab中优化使用(Yalmip latest document, can be used in MATLAB optimization)