New users and old of optimization in MATLAB will find useful tips and tricks in this document, as well as examples one can use as templates for their own problems. Use this tool by editing the file optimtips.m, then execute blocks of code in cell mode from the editor, or best, publish the file to HTML. Copy and paste also works of course. Some readers may find this tool valuable if only for the function pleas - a partitioned least Squares solver based on lsqnonlin. This is a work in progress, as I fully expect to add new topics as I think of them or as suggestions are made. Suggestions for topics I ve missed are welcome, as are corrections of my probable numerous errors. The topics currently covered are listed below
標(biāo)簽： optimization and document MATLAB
上傳時(shí)間： 2015-12-24
上傳用戶(hù)：佳期如夢(mèng)
We address the problem of blind carrier frequency-offset (CFO) estimation in quadrature amplitude modulation, phase-shift keying, and pulse amplitude modulation communications systems.We study the performance of a standard CFO estimate, which consists of first raising the received signal to the Mth power, where M is an integer depending on the type and size of the symbol constellation, and then applying the nonlinear least Squares (NLLS) estimation approach. At low signal-to noise ratio (SNR), the NLLS method fails to provide an accurate CFO estimate because of the presence of outliers. In this letter, we derive an approximate closed-form expression for the outlier probability. This enables us to predict the mean-square error (MSE) on CFO estimation for all SNR values. For a given SNR, the new results also give insight into the minimum number of samples required in the CFO estimation procedure, in order to ensure that the MSE on estimation is not significantly affected by the outliers.
標(biāo)簽： frequency-offset estimation quadrature amplitude
上傳時(shí)間： 2014-01-22
上傳用戶(hù)：牛布牛
This paper examines the asymptotic (large sample) performance of a family of non-data aided feedforward (NDA FF) nonlinear least-Squares (NLS) type carrier frequency estimators for burst-mode phase shift keying (PSK) modulations transmitted through AWGN and flat Ricean-fading channels. The asymptotic performance of these estimators is established in closed-form expression and compared with the modified Cram`er-Rao bound (MCRB). A best linear unbiased estimator (BLUE), which exhibits the lowest asymptotic variance within the family of NDA FF NLS-type estimators, is also proposed.
標(biāo)簽： performance asymptotic examines non-data
上傳時(shí)間： 2015-12-30
上傳用戶(hù)：225588
Ink Blotting One method for escaping from a maze is via ‘ink-blotting’. In this method your starting square is marked with the number ‘1’. All free, valid Squares north, south, east and west around the number ‘1‘ are marked with a number ‘2’. In the next step, all free, valid Squares around the two are marked with a ‘3’ and the process is repeated iteratively until : The exit is found (a free square other than the starting position is reached on the very edge of the maze), or, No more free Squares are available, and hence no exit is possible.
標(biāo)簽： method ink-blotting Blotting escaping
上傳時(shí)間： 2014-12-03
上傳用戶(hù)：123啊
PCA and PLS aims:to get some insight into the bilinear factor models Principal Component Analysis (PCA) and Partial Least Squares (PLS) regression, focusing on the mathematics and numerical aspects rather than how s and why s of data analysis practice. For the latter part it is assumed (but not absolutely necessary) that the reader is already familiar with these methods. It also assumes you have had some preliminary experience with linear/matrix algebra.
標(biāo)簽： Component Principal Analysis bilinear
上傳時(shí)間： 2016-02-07
上傳用戶(hù)：zuozuo1215
KMEANS Trains a k means cluster model.CENTRES = KMEANS(CENTRES, DATA, OPTIONS) uses the batch K-means algorithm to set the centres of a cluster model. The matrix DATA represents the data which is being clustered, with each row corresponding to a vector. The sum of Squares error function is used. The point at which a local minimum is achieved is returned as CENTRES.
標(biāo)簽： CENTRES KMEANS OPTIONS cluster
上傳時(shí)間： 2014-01-07
上傳用戶(hù)：zhouli
Numerical Computing with MATLAB （by Cleve Moler) is a textbook for an introductory course in numerical methods, Matlab, and technical computing. The emphasis is on in- formed use of mathematical software. We want you learn enough about the mathe- matical functions in Matlab that you will be able to use them correctly, appreciate their limitations, and modify them when necessary to suit your own needs. The topics include * introduction to Matlab, * linear equations, * interpolation, * zero and roots, * least Squares, * quadrature, * ordinary di?erential equations, * random numbers, * Fourier analysis, * eigenvalues and singular values, * partial di?erential equations.
標(biāo)簽： introductory Numerical Computing textbook
上傳時(shí)間： 2016-07-04
上傳用戶(hù)：思琦琦
Toolbox for Numerical Computing with MATLAB （by Cleve Moler). Numerical Computing with MATLAB （by Cleve Moler) is a textbook for an introductory course in numerical methods, Matlab, and technical computing. The emphasis is on in- formed use of mathematical software. We want you learn enough about the mathe- matical functions in Matlab that you will be able to use them correctly, appreciate their limitations, and modify them when necessary to suit your own needs. The topics include * introduction to Matlab, * linear equations, * interpolation, * zero and roots, * least Squares, * quadrature, * ordinary di?erential equations, * random numbers, * Fourier analysis, * eigenvalues and singular values, * partial differential equations.
標(biāo)簽： Numerical Computing MATLAB with
上傳時(shí)間： 2014-01-01
上傳用戶(hù)：guanliya
this directory contains the following: * The acdc algorithm for finding the approximate general (non-orthogonal) joint diagonalizer (in the direct Least Squares sense) of a set of Hermitian matrices. [acdc.m] * The acdc algorithm for finding the same for a set of Symmetric matrices. [acdc_sym.m](note that for real-valued matrices the Hermitian and Symmetric cases are similar however, in such cases the Hermitian version [acdc.m], rather than the Symmetric version[acdc_sym] is preferable. * A function that finds an initial guess for acdc by applying hard-whitening followed by Cardoso s orthogonal joint diagonalizer. Note that acdc may also be called without an initial guess, in which case the initial guess is set by default to the identity matrix. The m-file includes the joint_diag function (by Cardoso) for performing the orthogonal part. [init4acdc.m]
標(biāo)簽： approximate directory algorithm the
上傳時(shí)間： 2014-01-17
上傳用戶(hù)：hanli8870
The toolbox solves a variety of approximate modeling problems for linear static models. The model can be parameterized in kernel, image, or input/output form and the approximation criterion, called misfit, is a weighted norm between the given data and data that is consistent with the model. There are three main classes of functions in the toolbox: transformation functions, misfit computation functions, and approximation functions. The approximation functions derive an approximate model from data, the misfit computation functions are used for validation and comparison of models, and the transformation functions are used for deriving one model representation from another. KEYWORDS: Total least Squares, generalized total least Squares, software implementation.
標(biāo)簽： approximate The modeling problems
上傳時(shí)間： 2013-12-20
上傳用戶(hù)：15071087253
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