@InProceedings{Supelec543,
author = {Lucian Alecu and Hervé Frezza-Buet},
title = {Application-driven parameter tuning methodology for dynamic neural field equations},
year = {2009},
booktitle = { Neural Information Processing, ICONIP'09 Proceedings, Part I},
publisher = {Springer Berlin / Heidelberg},
volume = {5863/2009},
pages = {135-142},
series = {Lecture Notes in Computer Science},
address = {Bangkok (Thailand)},
url = {http://www.metz.supelec.fr/metz/personnel/alecu_luc/papers/iconip09.pdf},
isbn = {978-3-642-10676-7},
doi = {10.1007/978-3-642-10677-4_15},
abstract = {In this paper, a method is introduced in order to qualify the performance of dynamic neural fields (DNF). The method is applied to Amariís DNF equations, in order to drive the tuning of its free parameters. An original evaluation procedure is presented, and then applied to some input evolution scenarios. Such scenarios define an applicative context, for which the parameters with the lowest evaluation are optimal.}
}