Discussion of ""generalized regression neural networks for evapotranspiration modelling""

Koutsoyiannis, D

dc.contributor.author	Koutsoyiannis, D	en
dc.date.accessioned	2014-03-01T11:44:44Z
dc.date.available	2014-03-01T11:44:44Z
dc.date.issued	2007	en
dc.identifier.issn	0262-6667	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/37133
dc.subject	Artificial Neural Network	en
dc.subject	Central Nervous System	en
dc.subject	Complex System	en
dc.subject	Evapotranspiration	en
dc.subject	General Regression Neural Network	en
dc.subject	Model Complexity	en
dc.subject	Nonlinear Model	en
dc.subject	Nonlinear System	en
dc.subject	Optimal Method	en
dc.subject	Neural Network	en
dc.subject.classification	Water Resources	en
dc.title	Discussion of ""generalized regression neural networks for evapotranspiration modelling""	en
heal.type	other	en
heal.identifier.primary	10.1623/hysj.52.4.832	en
heal.identifier.secondary	http://dx.doi.org/10.1623/hysj.52.4.832	en
heal.language	English	en
heal.publicationDate	2007	en
heal.abstract	There is no doubt that so-called "artificial neural networks" (ANN) are powerful computational tools to model complex nonlinear systems. In my view, an ANN estab- lishes a data-driven nonlinear relationship between inputs and outputs of a system. The fact that such a nonlinear model is generally very complicated (usually one does not even write down the equations) renders it a	en
heal.publisher	IAHS PRESS, INST HYDROLOGY	en
heal.journalName	Hydrological Sciences Journal	en
dc.identifier.doi	10.1623/hysj.52.4.832	en
dc.identifier.isi	ISI:000248730300018	en
dc.identifier.volume	52	en
dc.identifier.issue	4	en
dc.identifier.spage	832	en
dc.identifier.epage	835	en