Ben

 

Comments and Ratings by Ben		View all 1 - 5 of 6
Updated	File	Comments	Rating
26 Nov 2009	Taylor Diagram Plot a Taylor Diagram Author: Guillaume MAZE	Thank you for the nice script and the examples. I just have one question. What kind of function is the 'ptable' in the example: ax = ptable([2 3],[2 2;4 6]); Thanks Cheers Ben
27 Oct 2009	Goodness of Fit (Modified) Computes goodness of fit for regression model given matrix/vector of target and output values. Author: Richard Crozier
04 Sep 2009	MyRegression A simple function on LS linear regression with many informative outputs Author: Giuseppe Cardillo
25 Aug 2009	Windowed Cross Correlation (corrgram) Computes windowed cross correlation Author: Norbert Marwan	I agree
21 Aug 2009	Testt Calculate the Student t Test for unequal or equal samples size, unpaired or paired samples. Author: Giuseppe Cardillo	Thanks

Files Matching Ben's Watch List					View all 1 - 2 of 2
Updated		File	Tags	Downloads (last 30 days)	Comments	Rating
12 Oct 2005		Learning the Kalman Filter Basic Kalman filter, heavily commented, for beginners to Kalman filtering. Author: Michael Kleder	basic kalman filter, kalman filter, discrete filter, linear, imtransform, filter analysis	659	87	4.4375 4.4 | 81 ratings
05 Jul 2005		Goodness of Fit Computes goodness of fit (model performance) for regression. Author: Durga Lal Shrestha	model performance, goodness, fit, mathematics	20	3	4.0 4.0 | 2 ratings

Comments and Ratings Matching Ben's Watch List			1-5 of 122
Date	File	Comment by	Comments	Rating
05 Aug 2011	Learning the Kalman Filter Basic Kalman filter, heavily commented, for beginners to Kalman filtering.	greg	Nicely documented. As a practicing engineer I would never use the implementation shown. This is the standard covariance form of the Kalman filter. In operation the statement s.P = s.P - K*s.H*s.P; causes significant issues. s.P needs to always be positive definite but with rounding this will tend to violate this assmption making the Kalman filter 'blow up' over time or with poorly conditioned data. The alternative is to process in the square root domain where the P matrix is expressed as a P=Psr'*Psr. Therefore the resuting matrix must always be positive definite and does not have this issue. An added advantage is the precision is doubled processing in this domain and can be similar to the input data resolution rather than 2x the number of bits to provide comparable precision. For more information see 'Linear estimation' Kailath, Sayed, Hassibi or 'adaptive filter thoery' Haykin IMHO one should always to filtering in the SR domain. There are also advantages of processing with separated real/imag data rather then complex if the underling data is complex.	Comment only
22 Jul 2011	Learning the Kalman Filter Basic Kalman filter, heavily commented, for beginners to Kalman filtering.	prokash paul	nice doc	5
16 Jul 2011	Learning the Kalman Filter Basic Kalman filter, heavily commented, for beginners to Kalman filtering.	lotfi	good !	Comment only
27 Jun 2011	Learning the Kalman Filter Basic Kalman filter, heavily commented, for beginners to Kalman filtering.	Edgar lobachevskiy	excellent!! Thank you!!	5
02 Jun 2011	Learning the Kalman Filter Basic Kalman filter, heavily commented, for beginners to Kalman filtering.	Liu Kefeng	thank you very much!	5