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argument of maximum, rayleigh

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20 May 2009		ARGMAX/ARGMIN This set of functions efficiently compute argument of maximum/minimum for vectors and matrices. Author: Marco Cococcioni	argument of maximum, argument of minimum, matematics, matrix operators	38	0
21 Feb 2008		A Matlab-based Object-Oriented Approach to Multipath Fading Channel Simulation This white paper explains the algorithms used in the Communications Toobox's fading channel function Author: Cyril Iskander	jakes, fading, rayleigh, multipath, rician, wireless	87	8	5.0 5.0 | 5 ratings

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27 Nov 2008		RMSE calculates root mean square error from data vector or matrix and the corresponding estimates. Author: Felix Hebeler	root mean square erro..., rmse, mathematics, general, scatter	75	7	4.0 4.0 | 2 ratings

Comments and Ratings Matching shanghai Normal University's Watch List			1-5 of 8
Date	File	Comment by	Comments	Rating
12 Jun 2011	RMSE calculates root mean square error from data vector or matrix and the corresponding estimates.	Hassan Naseri	I always use mean function instead of sum and divide rms = sqrt(mean((data(:)-estimate(:)).^2));	Comment only
08 Mar 2010	RMSE calculates root mean square error from data vector or matrix and the corresponding estimates.	Andre Guy Tranquille		4
27 Oct 2008	RMSE calculates root mean square error from data vector or matrix and the corresponding estimates.	Wolfgang Schwanghart	Hi Felix and Gary, yes, the two sums could be avoided by simply writing r=sqrt(sum((data(:)-estimate(:)).^2)/numel(data)) The computation time is about the same but readability might be enhanced by using the colon operator. Best regards, Wolfgang	Comment only
10 Oct 2008	RMSE calculates root mean square error from data vector or matrix and the corresponding estimates.	Felix Hebeler	@Gary: no, you need two sums if you process matrices, the first sums across all columns, the second then sums across the resulting vector. If you process vectors, the second sum calculates the sum of a scalar. Faster than checking for dimensions first.	Comment only
09 Oct 2008	RMSE calculates root mean square error from data vector or matrix and the corresponding estimates.	Gary Merkoske	you have one too many SUM() in the eqn, although it appears to be harmless. Am I correct? RMS Error is then; r=sqrt(sum((data-estimate).^2)/numel(data))	4