DocumentCode
1660230
Title
Error estimation and error bounds for neural networks
Author
Liang, Hualou ; Dai, Guiliang
Author_Institution
Comput. Center, Acad. Sinica, Beijing, China
fYear
1995
Firstpage
42
Lastpage
44
Abstract
A method is proposed to estimate the standard error of predicted values in multilayer perceptron (MLP). It is based on likelihood theory. It holds for all feedforward networks, irrespective of the topology or the specific task at hand. In addition, the bounds on a neural network with perturbed weights and inputs is analytically derived. The bounds obtained are applicable to both digital and analog network implementations. By computer simulation, the validity of the proposed methods has been illustrated
Keywords
error analysis; feedforward neural nets; learning (artificial intelligence); maximum likelihood estimation; multilayer perceptrons; analog network; computer simulation; digital network; error bounds; error estimation; feedforward networks; likelihood theory; multilayer perceptron; neural networks; perturbed weights; predicted values; Error analysis; Indium phosphide; Neural networks; Physics;
fLanguage
English
Publisher
ieee
Conference_Titel
Artificial Neural Networks and Expert Systems, 1995. Proceedings., Second New Zealand International Two-Stream Conference on
Conference_Location
Dunedin
Print_ISBN
0-8186-7174-2
Type
conf
DOI
10.1109/ANNES.1995.499435
Filename
499435
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