Title :
Error estimation and error bounds for neural networks
Author :
Liang, Hualou ; Dai, Guiliang
Author_Institution :
Comput. Center, Acad. Sinica, Beijing, China
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;
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
DOI :
10.1109/ANNES.1995.499435
