GNU Octave is a high-level language, primarily intended for numerical computations. It provides a convenient command line interface for solving LINEAR and nonLINEAR problems numerically.
標(biāo)簽： computations high-level primarily numerical
上傳時(shí)間： 2014-01-07
上傳用戶(hù)：星仔
The tca package is a Matlab program that implements the tree-dependent component analysis (TCA) algorithms that extends the independent component analysis (ICA), where instead of looking for a LINEAR transform that makes the data components independent, we are looking for components that can be best fitted in a tree structured graphical model. The TCA model can be applied in any situation where the data can be assumed to have been transformed by an unknown LINEAR transformation.
標(biāo)簽： tree-dependent implements component analysis
上傳時(shí)間： 2016-09-17
上傳用戶(hù)：cazjing
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
標(biāo)簽： identification considered features separati
上傳時(shí)間： 2016-09-20
上傳用戶(hù)：FreeSky
英文版G.729語(yǔ)音壓縮標(biāo)準(zhǔn)。 GENERAL ASPECTS OF DIGITAL TRANSMISSION SYSTEMS CODING OF SPEECH AT 8 kbit/s USING CONJUGATE-STRUCTURE ALGEBRAIC-CODE-EXCITED LINEAR-PREDICTION (CS-ACELP)
標(biāo)簽： TRANSMISSION GENERAL ASPECTS DIGITAL
上傳時(shí)間： 2016-09-29
上傳用戶(hù)：15071087253
用Java實(shí)現(xiàn)的一個(gè)簡(jiǎn)單的寄存器分配器，用的算法是線性掃描(LINEAR Scan)
上傳時(shí)間： 2013-12-10
上傳用戶(hù)：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
標(biāo)簽： gradient function provided versions
上傳時(shí)間： 2016-11-07
上傳用戶(hù)：秦莞爾w
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.
標(biāo)簽： introduces Quincunx Optimal MATLAB
上傳時(shí)間： 2014-01-15
上傳用戶(hù)：cc1
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.
標(biāo)簽： cross-entropy introduce smoothing addition
上傳時(shí)間： 2014-01-06
上傳用戶(hù)：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. ％
標(biāo)簽： back-propagation corresponding input-output algorithm
上傳時(shí)間： 2016-12-27
上傳用戶(hù)：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.
標(biāo)簽： Levenberg-Marquardt desired network neural
上傳時(shí)間： 2016-12-27
上傳用戶(hù)：jcljkh
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