Description Usage Arguments Value Author(s) References See Also
The error functions calculate the goodness of fit of a neural network according to certain criterium:
LMS: Least Mean Squares Error.
LMLS: Least Mean Log Squares minimization.
TAO: TAO error minimization.
The deltaE functions calculate the influence functions of their error criteria.
1 2 3 4 5 6 | error.LMS(arguments)
error.LMLS(arguments)
error.TAO(arguments)
deltaE.LMS(arguments)
deltaE.LMLS(arguments)
deltaE.TAO(arguments)
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arguments |
List of arguments to pass to the functions.
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This functions return the error and influence function criteria.
Manuel Castejón Limas. manuel.castejon@gmail.com
Joaquin Ordieres Meré. j.ordieres@upm.es
Ana González Marcos. ana.gonzalez@unirioja.es
Alpha V. Pernía Espinoza. alpha.pernia@unirioja.es
Francisco Javier Martinez de Pisón. fjmartin@unirioja.es
Fernando Alba Elías. fernando.alba@unavarra.es
Pernía Espinoza, A.V., Ordieres Meré, J.B., Martínez de Pisón, F.J., González Marcos, A. TAO-robust backpropagation learning algorithm. Neural Networks. Vol. 18, Issue 2, pp. 191–204, 2005.
Simon Haykin. Neural Networks – a Comprehensive Foundation. Prentice Hall, New Jersey, 2nd edition, 1999. ISBN 0-13-273350-1.
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