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
标签： identification considered features separati
上传时间： 2016-09-20
上传用户：FreeSky
英文版G.729语音压缩标准。 GENERAL ASPECTS OF DIGITAL TRANSMISSION SYSTEMS CODING OF SPEECH AT 8 kbit/s USING CONJUGATE-STRUCTURE ALGEBRAIC-CODE-EXCITED LINEAR-PREDICTION (CS-ACELP)
标签： TRANSMISSION GENERAL ASPECTS DIGITAL
上传时间： 2016-09-29
上传用户：15071087253
用Java实现的一个简单的寄存器分配器，用的算法是线性扫描(Linear Scan)
上传时间： 2013-12-10
上传用户：zyt
The inverse of the gradient function. I ve provided versions that work on 1-d vectors, or 2-d or 3-d arrays. In the 1-d case I offer 5 different methods, from cumtrapz, and an integrated cubic spline, plus several finite difference methods. In higher dimensions, only a finite difference/linear algebra solution is provided, but it is fully vectorized and fully sparse in its approach. In 2-d and 3-d, if the gradients are inconsistent, then a least squares solution is generated
标签： gradient function provided versions
上传时间： 2016-11-07
上传用户：秦莞尔w
简单有限元计算程序。 NJF---结点自由度数 E---弹性模量 NE---单元数 T---板厚度 VM---泊松比 z---实型二维数组，用来存放结点坐标 ZJ---实型二维数组，用来存放每个约束自由度方向和该方向的约束值， ie---实型二维数组，存放布尔矩阵 p---实型一维数组，先存放等效结点载荷，解完方程后存放输出的结点位移 a,b,a---实型数组，存放各个单元的面积及由结点坐标计算的bi,bj,ci,cj g---实型数组,存放单刚子块 zk---实型二维数组，存放整刚矩阵
上传时间： 2014-01-24
上传用户：cuibaigao
MATLAB Code for Optimal Quincunx Filter Bank Design Yi Chen July 17, 2006 This file introduces the MATLAB code that implements the two algorithms (i.e., Algorithms 1 and 2 in [1], or Algorithms 4.1 and 4.2 in [2]) used for the construction of quincunx filter banks with perfect reconstruction, linear phase, high coding gain, certain vanishing moments properties, and good frequency selectivity. The code can be used to design quincunx filter banks with two, three, or four lifting steps. The SeDuMi Matlab toolbox [3] is used to solve the second-order cone programming subproblems in the two algorithms, and must be installed in order for this code to work.
标签： introduces Quincunx Optimal MATLAB
上传时间： 2014-01-15
上传用户：cc1
两台处理机A 和B处理n个作业。设第i个作业交给机器 A 处理时需要时间ai，若由机器B 来处理，则需要时间bi。由于各作 业的特点和机器的性能关系，很可能对于某些i，有ai >=bi，而对于 某些j，j!=i，有aj<bj。既不能将一个作业分开由两台机器处理，也没 有一台机器能同时处理2 个作业。设计一个动态规划算法，使得这两 台机器处理完成这n 个作业的时间最短（从任何一台机器开工到最后 一台机器停工的总时间）。研究一个实例：（a1,a2,a3,a4,a5,a6）= （2,5,7,10,5,2）；（b1,b2,b3,b4,b5,b6）=（3,8,4,11,3,4）
上传时间： 2014-01-14
上传用户：独孤求源
measure through the cross-entropy of test data. In addition, we introduce two novel smoothing techniques, one a variation of Jelinek-Mercer smoothing and one a very simple linear interpolation technique, both of which outperform existing methods.
标签： cross-entropy introduce smoothing addition
上传时间： 2014-01-06
上传用户：qilin
Batch version of the back-propagation algorithm. ％ Given a set of corresponding input-output pairs and an initial network ％ [W1,W2,critvec,iter]=batbp(NetDef,W1,W2,PHI,Y,trparms) trains the ％ network with backpropagation. ％ ％ The activation functions must be either linear or tanh. The network ％ architecture is defined by the matrix NetDef consisting of two ％ rows. The first row specifies the hidden layer while the second ％ specifies the output layer. ％
标签： back-propagation corresponding input-output algorithm
上传时间： 2016-12-27
上传用户：exxxds
％ Train a two layer neural network with the Levenberg-Marquardt ％ method. ％ ％ If desired, it is possible to use regularization by ％ weight decay. Also pruned (ie. not fully connected) networks can ％ be trained. ％ ％ Given a set of corresponding input-output pairs and an initial ％ network, ％ [W1,W2,critvec,iteration,lambda]=marq(NetDef,W1,W2,PHI,Y,trparms) ％ trains the network with the Levenberg-Marquardt method. ％ ％ The activation functions can be either linear or tanh. The ％ network architecture is defined by the matrix NetDef which ％ has two rows. The first row specifies the hidden layer and the ％ second row specifies the output layer.
标签： Levenberg-Marquardt desired network neural
上传时间： 2016-12-27
上传用户：jcljkh
