Abstract
A reliable predictor is very useful for real-world industrial applications to forecast thefuture behavior of dynamic systems. A smart predictor, based on a novel recurrent neuralfuzzy (RNF) scheme, is developed in this paper for multi-step-ahead prediction of materialproperties. A systematic investigation based on two benchmark data sets is conducted interms of performance and efficiency. Analysis results reveal that, of the data-drivenforecasting schemes, predictors based on step input patterns outperform those based onsequential input patterns; the RNF predictor outperforms those based on recurrent neuralnetworks and ANFIS schemes in multi-step-ahead prediction of nonlinear time series. Anadaptive Levenberg–Marquardt training technique is adopted to improve therobustness and convergence of the RNF predictor. Furthermore, the proposed smartpredictor is implemented for material property testing. Investigation results show thatthe developed RNF predictor is a reliable forecasting tool for material propertytesting; it can capture and track the system’s dynamic characteristics quickly andaccurately. It is also a robust predictor to accommodate different system conditions.
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