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Application of Hybrid Learning Neural Fuzzy Systems in Reliability Prediction
Author(s) -
Pai PingFeng,
Lin KuoPing
Publication year - 2006
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.696
Subject(s) - artificial neural network , artificial intelligence , reliability (semiconductor) , computer science , neuro fuzzy , machine learning , multilayer perceptron , fuzzy logic , adaptive neuro fuzzy inference system , autoregressive integrated moving average , conjugate gradient method , hybrid system , fuzzy control system , time series , algorithm , power (physics) , physics , quantum mechanics
This study presents a hybrid learning neural fuzzy system for accurately predicting system reliability. Neural fuzzy system learning with and without supervision has been successfully applied in control systems and pattern recognition problems. This investigation modifies the hybrid learning fuzzy systems to accept time series data and therefore examines the feasibility of reliability prediction. Two neural network systems are developed for solving different reliability prediction problems. Additionally, a scaled conjugate gradient learning method is applied to accelerate the training in the supervised learning phase. Several existing approaches, including feed‐forward multilayer perceptron (MLP) networks, radial basis function (RBF) neural networks and Box–Jenkins autoregressive integrated moving average (ARIMA) models, are used to compare the performance of the reliability prediction. The numerical results demonstrate that the neural fuzzy systems have higher prediction accuracy than the other methods. Copyright © 2005 John Wiley & Sons, Ltd.