least squares method
上傳時間： 2014-01-25
上傳用戶：lifangyuan12
System identification with adaptive filter using full and partial-update Recursive-least-squares
標簽： Recursive-least-squares identification partial-update adaptive
上傳時間： 2013-12-30
上傳用戶：LouieWu
MATLAB Example Code : Non-Linear Least Squares --- Bearings-Only Measurement
標簽： Bearings-Only Measurement Non-Linear Example
上傳時間： 2014-06-08
上傳用戶：fxf126@126.com
直線擬合的幾種算法，其中包括線性最小二乘，和兩種不同目標函數的非線性最小二乘，用于比較這些方法的優劣，另外matlab中說的robust least squares方法沒有找到，希望有朋友能給穿一下：）
上傳時間： 2014-06-18
上傳用戶：大三三
This program demonstrates some function approximation capabilities of a Radial Basis Function Network. The user supplies a set of training points which represent some "sample" points for some arbitrary curve. Next, the user specifies the number of equally spaced gaussian centers and the variance for the network. Using the training samples, the weights multiplying each of the gaussian basis functions arecalculated using the pseudo-inverse (yielding the minimum least-squares solution). The resulting network is then used to approximate the function between the given "sample" points.
標簽： approximation demonstrates capabilities Function
上傳時間： 2014-01-01
上傳用戶：zjf3110
A fast customizable function for locating and measuring the peaks in noisy time-series signals. Adjustable parameters allow discrimination of "real" signal peaks from noise and background. Determines the position, height, and width of each peak by least-squares curve-fitting.
標簽： customizable time-series measuring function
上傳時間： 2015-08-10
上傳用戶：invtnewer
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
標簽： optimization and document MATLAB
上傳時間： 2015-12-24
上傳用戶：佳期如夢
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.
標簽： frequency-offset estimation quadrature amplitude
上傳時間： 2014-01-22
上傳用戶：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.
標簽： performance asymptotic examines non-data
上傳時間： 2015-12-30
上傳用戶：225588
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.
標簽： Component Principal Analysis bilinear
上傳時間： 2016-02-07
上傳用戶：zuozuo1215
