Description: The main features of the considered identification problem are that there is no an a priori separation of the variables into inputs and outputs and the approximation criterion, called misfit, does not depend on the model representation. The misfit is defined as the minimum of the l2-norm between the given time series and a time series that is consistent with the approximate model. The misfit is equal to zero if and only if the model is exact and the smaller the misfit is (by definition) the more accurate the model is. The considered model class consists of all linear time-invariant systems of bounded complexity and the complexity is specified by the number of inputs and the smallest number of lags in a difference equation representation. We present a Matlab function for approximate identification based on misfit minimization. Although the problem formulation is representation independent, we use input/state/output representations of the system in order Platform: |
Size: 1031113 |
Author:kedle |
Hits:
Description: 实现聚类K均值算法： K均值算法：给定类的个数K，将n个对象分到K个类中去，使得类内对象之间的相似性最大，而类之间的相似性最小。 缺点：产生类的大小相差不会很大，对于脏数据很敏感。 改进的算法：k—medoids 方法。这儿选取一个对象叫做mediod来代替上面的中心 的作用，这样的一个medoid就标识了这个类。步骤： 1，任意选取K个对象作为medoids（O1,O2,…Oi…Ok）。 以下是循环的： 2，将余下的对象分到各个类中去（根据与medoid最相近的原则）； 3，对于每个类（Oi）中，顺序选取一个Or，计算用Or代替Oi后的消耗—E（Or）。选择E最小的那个Or来代替Oi。这样K个medoids就改变了，下面就再转到2。 4，这样循环直到K个medoids固定下来。 这种算法对于脏数据和异常数据不敏感，但计算量显然要比K均值要大，一般只适合小数据量。-achieving K-mean clustering algorithms : K-means algorithm : given the number of Class K, n will be assigned to target K to 000 category, making target category of the similarity between the largest category of the similarity between the smallest. Disadvantages : class size have no great difference for dirty data is very sensitive. Improved algorithms : k-medoids methods. Here a selection of objects called mediod to replace the center of the above, the logo on a medoid this category. Steps : 1, arbitrary selection of objects as K medoids (O1, O2, Ok ... ... Oi). Following is a cycle : 2, the remaining targets assigned to each category (in accordance with the closest medoid principle); 3, for each category (Oi), the order of selection of a Or, calculated Oi Or replace the consumption-E (Or) Platform: |
Size: 1024 |
Author:阿兜 |
Hits:
Description: 实现聚类K均值算法： K均值算法：给定类的个数K，将n个对象分到K个类中去，使得类内对象之间的相似性最大，而类之间的相似性最小。-achieving K-mean clustering algorithms : K-means algorithm : given the number of Class K, n objects assigned K to 000 category, making such objects within the similarity between the largest category of the similarity between the smallest. Platform: |
Size: 1024 |
Author:yili |
Hits:
Description: 关于最小换乘次数的最佳公交路线模型的matlab程序,
关键词:广度优化搜索 最佳路线 换车次数-The smallest change on the frequency of the best bus route model matlab procedures, Keywords: breadth optimize the best route to change the number of search Platform: |
Size: 7168 |
Author:kkktewq |
Hits:
Description: 1) Write a function reverse(A) which takes a matrix A of arbitrary dimensions as input and returns a matrix B consisting of the columns of A in reverse order. Thus for example, if
A = 1 2 3 then B = 3 2 1
4 5 6 6 5 4
7 8 9 9 8 7
Write a main program to call reverse(A) for the matrix A = magic(5). Print to the screen both A and reverse(A).
2) Write a program which accepts an input k from the keyboard, and which prints out the smallest fibonacci number that is at least as large as k. The program should also print out its position in the fibonacci sequence. Here is a sample of input and output:
Enter k>0: 100
144 is the smallest fibonacci number greater than or equal to 100.
It is the 12th fibonacci number.
Platform: |
Size: 1024 |
Author:wangshujuan |
Hits:
Description: 递归式最小均方（RLS）算法的基本思想是力图使在每个时刻对所有已输入信号而言重估的平方误差的加权和最小，这使得RLS算法对非平稳信号的适应性要好。与LMS算法相比，RLS算法采用时间平均，因此，所得出的最优滤波器依赖于用于计算平均值的样本数，而LMS（NLMS）算法是基于集平均而设计的，因此稳定环境下LMS（NLMS）算法在不同计算条件下的结果是一致的-Recursive least-mean-square (RLS) algorithm for the basic idea is to try to make in every moment of all the input signal in terms of re-evaluation of the weighted squared error and the smallest, which allows non-stationary RLS algorithm for adaptive signal better. Compared with the LMS algorithm, RLS algorithm uses the average time, therefore, the resulting optimal filter depends on the used to calculate the average number of samples, and the LMS (NLMS) algorithm is designed based on set average, and therefore a stable environment LMS (NLMS) algorithm in different conditions, the results of the calculation is consistent Platform: |
Size: 3072 |
Author:闫丰 |
Hits:
Description: 经典的一维装箱问题(Bin Packing Problem)是指，给定 件物品的序列 ，物品 的大小 ，要求将这些物品装入单位容量1的箱子 中，使得每个箱子中的物品大小之和不超过1，并使所使用的箱子数目 最小。-Classic one-dimensional bin-packing problem (Bin Packing Problem) means that a given sequence of items, item size, request these items into one unit of capacity of the box, making the items for each box size and non- more than one, and the number used in the smallest box. Platform: |
Size: 3072 |
Author:chj |
Hits:
Description: The main features of the considered identification problem are that there is no an a priori separation of the variables into inputs and outputs and the approximation criterion, called misfit, does not depend on the model representation. The misfit is defined as the minimum of the l2-norm between the given time series and a time series that is consistent with the approximate model. The misfit is equal to zero if and only if the model is exact and the smaller the misfit is (by definition) the more accurate the model is. The considered model class consists of all linear time-invariant systems of bounded complexity and the complexity is specified by the number of inputs and the smallest number of lags in a difference equation representation. We present a Matlab function for approximate identification based on misfit minimization. Although the problem formulation is representation independent, we use input/state/output representations of the system in order -The main features of the considered identification problem are that there is no an a priori separation of the variables into inputs and outputs and the approximation criterion, called misfit, does not depend on the model representation. The misfit is defined as the minimum of the l2-norm between the given time series and a time series that is consistent with the approximate model. The misfit is equal to zero if and only if the model is exact and the smaller the misfit is (by definition) the more accurate the model is. The considered model class consists of all linear time-invariant systems of bounded complexity and the complexity is specified by the number of inputs and the smallest number of lags in a difference equation representation. We present a Matlab function for approximate identification based on misfit minimization. Although the problem formulation is representation independent, we use input/state/output representations of the system in order Platform: |
Size: 1031168 |
Author:kedle |
Hits:
Description: 一些关于图像阈值确定的matlab程序，包括迭代阈值，最小类内方差，最大熵，和用matlab库函数对图像进行边缘检测。可运行-Number of image thresholding matlab identified procedures, including iterative threshold, the smallest category of variance, maximum entropy, and the use of matlab library function of the image edge detection. Run Platform: |
Size: 30720 |
Author:刘大专 |
Hits:
Description: 多旅行商问题是单旅行商问题的扩展, 具有更广泛的实际意义。在研究M TSP 解的特点的基础上, 提
出了最小化总行程和均分多个旅行商访问点数、最小化总行程及均分访问路程的两个多目标的M TSP 问题,
并分别给出了相应的数学模型、求解算法和应用实例, 实例表明模型的正确性。-Multi-traveling salesman problem traveling salesman problem is a single expansion, with a wider range of practical significance. M TSP in the study of the characteristics of solutions based on the smallest share of the total number of traveling salesman tour and visit the points, the smallest share of the total trip distance to visit more than two goals of the M TSP problem and were given the corresponding mathematical model, algorithm and application examples, examples show that the model is correct. Platform: |
Size: 242688 |
Author:Notics |
Hits:
Description: 用最速下降法求最优化解
输入：f为函数名 grad为梯度函数
x0为解的初值 TolX,TolFun分别为变量和函数的误差阈值
dist0为初始步长 MaxIter为最大迭代次数
输出: xo为取最小值的点 fo为最小的函数值
f0 = f(x(0- Steepest Descent Method with Optimum Solution input: f as a function name grad is gradient function x0 for the solution of the initial TolX, TolFun variables and functions were error threshold dist0 as the initial step MaxIter maximum Diego passage number Output: xo to take the minimum point of fo is the smallest function value f0 = f (x (0)) Platform: |
Size: 1024 |
Author:华 |
Hits:
Description: 最优分割的基本思路是给定一个数据序列和分层数，通过搜索所有可能的划分方案，找到段内离差平方和的总和最小的一种方案作为最终划分方案。这里给出最优分割法的Matlab源程序-The basic idea is optimal partition for a given data sequence and hierarchical number divided by searching all possible solutions to find the segment and the sum of the squared deviations smallest division of a scheme as a final solution. This gives the optimal segmentation Matlab source Platform: |
Size: 1024 |
Author:lucy |
Hits:
Search - smallest number matlab