Hierarchical RBF networks and local parameters estimate

Autor:	Stefano Ferrari, Nunzio Alberto Borghese
Rok vydání:	1998
Předmět:	Mathematical optimization Parallelizable manifold Cognitive Neuroscience Gaussian Residual Computer Science Applications symbols.namesake Data point Function approximation Artificial Intelligence symbols Maximum a posteriori estimation Algorithm Linear filter Hierarchical RBF Mathematics
Zdroj:	Neurocomputing. 19:259-283
ISSN:	0925-2312
Popis:	The method presented here is aimed to a direct fast setting of the parameters of a RBF network for function approximation. It is based on a hierarchical gridding of the input space; additional layers of Gaussians at lower scales are added where the residual error is higher. The number of the Gaussians of each layer and their variance are computed from considerations grounded in the linear filtering theory. The weight of each Gaussian is estimated through a maximum a posteriori estimate carried out locally on a sub-set of the data points. The method shows a high accuracy in the reconstruction, it can deal with non-evenly spaced data points and can be fully parallelizable. Results on the reconstruction of both synthetic and real data are presented and discussed. ( 1998 Elsevier Science B.V. All rights reserved.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::6ceee6481306d617ecd35e0a203ea97b https://doi.org/10.1016/s0925-2312(97)00094-5 Zobrazit plný text záznamu Full Text from ScienceDirect