Abstract
In this paper, an adaptive neuro-fuzzy (NF) forecasting system is proposed, and its robustness is investigated experimentally. After the NF predictor is initially trained using a data set from the Mackey-Glass differential equation, it is implemented for two applications, an online gear system condition monitoring and a material fatigue testing to forecast future states of a fatigue crack propagation trend in test specimens. From the forecasting tests and simulation analyses, it is found that the developed NF system is a very reliable prognostic scheme; it can capture system dynamic behavior quickly, and track system responses accurately.
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© 2005 Springer-Verlag Berlin Heidelberg
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Wang, W. (2005). An Intelligent System for Dynamic System State Forecasting. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427445_75
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DOI: https://doi.org/10.1007/11427445_75
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25913-8
Online ISBN: 978-3-540-32067-8
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