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The "GEE! It s Simple" package illustrates Gaussian elimination with partial pivoting, which produces a factorization of P*A into the product L*U where P is a permutation MATRIX, and L and U are lower and upper triangular, respectively.
The functions in this package are accurate, but they are far slower than their MATLAB equivalents (x=A\b, [L,U,p]=lu(A), and so on). They are presented here merely to illustrate and educate. "Real" production code should use backslash and lu, not this package.
標(biāo)簽：
illustrates
elimination
Gaussian
pivoting
上傳時(shí)間：
2016-11-09
上傳用戶(hù)：wang5829
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The "GEE! It s Simple" package illustrates Gaussian elimination with partial pivoting, which produces a factorization of P*A into the product L*U where P is a permutation MATRIX, and L and U are lower and upper triangular, respectively.
The functions in this package are accurate, but they are far slower than their MATLAB equivalents (x=A\b, [L,U,p]=lu(A), and so on). They are presented here merely to illustrate and educate. "Real" production code should use backslash and lu, not this package.
標(biāo)簽：
illustrates
elimination
Gaussian
pivoting
上傳時(shí)間：
2014-01-21
上傳用戶(hù)：lxm
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This module provides an interface to an alphanumeric display module.
The current version of this driver supports any alphanumeric LCD module based on the:Hitachi HD44780 DOT MATRIX LCD controller.
標(biāo)簽：
module
alphanumeric
interface
provides
上傳時(shí)間：
2013-12-04
上傳用戶(hù)：himbly
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書(shū)系統(tǒng)地介紹MATLAB 7.0的混合編程方法和技巧。全書(shū)共分為13章。第1章和第2章介紹MATLAB的基礎(chǔ)知識(shí)，第3章簡(jiǎn)要介紹MATLAB混合編程，第4章至第9章分別介紹幾種典型的混合編程方法，包括C-MEX、MATLAB引擎、MAT數(shù)據(jù)文件共享、Mideva、MATRIX和Add-in。第10章、第11章介紹MATLAB與Delphi和Excel的混合編程。第12章介紹MATLAB COM Builder，第13章以圖像處理為例介紹了一個(gè)綜合應(yīng)用實(shí)例。
本書(shū)按混合編程的具體方法進(jìn)行邏輯編排，自始至終用實(shí)例描述，每章著重闡述各種混合編程方法的實(shí)質(zhì)和要點(diǎn)，同時(shí)穿插了作者多年使用MATLAB的經(jīng)驗(yàn)和體會(huì)。本書(shū)既適合初學(xué)者自學(xué)，也適用于高級(jí)MATLAB用戶(hù)，可作為高等數(shù)學(xué)、計(jì)算機(jī)、電子工程、數(shù)值分析、信息工程等課程的教學(xué)參考書(shū)，也可供上述領(lǐng)域的科研工作者參考。
本書(shū)所附光盤(pán)內(nèi)容詳盡、實(shí)例豐富，包含MATLAB實(shí)例的源文件、函數(shù)/命令和注解以及程序?qū)嵗?/p>
標(biāo)簽：
MATLAB
7.0
混合編程
上傳時(shí)間：
2013-12-24
上傳用戶(hù)：一諾88
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the text file QMLE contains the quasi maximum
likelyhood estimating procedure and performing Information MATRIX test
for a univariate GARCH(1,1) model
標(biāo)簽：
estimating
likelyhood
performing
the
上傳時(shí)間：
2014-11-22
上傳用戶(hù)：zhenyushaw
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This toolbox was designed as a teaching aid, which matlab is
particularly good for since source code is relatively legible and
simple to modify. However, it is still reasonably fast if used
with the supplied optimiser. However, if you really want to speed
things up you should consider compiling the MATRIX composition
routine for H into a mex function. Then again if you really want
to speed things up you probably shouldn t be using matlab
anyway... Get hold of a dedicated C program once you understand
the algorithm.
標(biāo)簽：
particularly
designed
teaching
toolbox
上傳時(shí)間：
2016-11-25
上傳用戶(hù)：hustfanenze
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PRINCIPLE: The UVE algorithm detects and eliminates from a PLS model (including from 1 to A components) those variables that do not carry any relevant information to model Y. The criterion used to trace the un-informative variables is the reliability of the regression coefficients: c_j=mean(b_j)/std(b_j), obtained by jackknifing. The cutoff level, below which c_j is considered to be too small, indicating that the variable j should be removed, is estimated using a MATRIX of random variables.The predictive power of PLS models built on the retained variables only is evaluated over all 1-a dimensions =(yielding RMSECVnew).
標(biāo)簽：
from
eliminates
PRINCIPLE
algorithm
上傳時(shí)間：
2016-11-27
上傳用戶(hù)：凌云御清風(fēng)
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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
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％ 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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Train a two layer neural network with a recursive prediction error
％ algorithm ("recursive Gauss-Newton"). Also pruned (i.e., not fully
％ connected) networks can be trained.
％
％ The activation functions can either be linear or tanh. The network
％ architecture is defined by the MATRIX NetDef , which has of two
％ rows. The first row specifies the hidden layer while the second
％ specifies the output layer.
標(biāo)簽：
recursive
prediction
algorithm
Gauss-Ne
上傳時(shí)間：
2016-12-27
上傳用戶(hù)：ljt101007
