Enhanced fuzzy-filtered neural networks for material fatigue prognosis

Authors:
Dezhi Li;Wilson Wang;Fathy Ismail
Affiliations:
Department of Mechanical & Mechatronics Engineering, University of Waterloo, Waterloo, ON, Canada N2L 3G1;Department of Mechanical Engineering, Lakehead University, Thunder Bay, ON, Canada P7B 5E1;Department of Mechanical & Mechatronics Engineering, University of Waterloo, Waterloo, ON, Canada N2L 3G1
Venue:
Applied Soft Computing
Year:
2013

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Abstract

Although fuzzy-filtered neural networks (FFNN) have been used in pattern classification because of their unique characteristics in feature extraction, they usually have poor performance in forecasting applications due to their structure complexities especially in their consequent reasoning part. In this paper, an enhanced FFNN, EFFNN, is proposed for time series forecasting and material fatigue prognosis. A novel neural network scheme is developed to facilitate computation implementation. A new conjugate technique is proposed to improve training efficiency. The effectiveness of the developed EFFNN scheme and the related training technique is demonstrated by a series of simulation tests. The EFFNN is also implemented for material fatigue prognosis. Test results show that the developed EFFNN predictor is an effective forecasting tool; it can capture system dynamics effectively and track system characteristics accurately.