Abstract
A reliable and real-time predictor is very useful to a wide array of industries to forecast the behaviour of dynamic systems. In this paper, an adaptive predictor is developed based on the neuro-fuzzy approach to dynamic system forecasting. An adaptive training technique is proposed to improve forecasting performance, accommodate different operation conditions, and prevent possible trapping due to local minima. The viability of the developed predictor is evaluated by using both gear system condition monitoring and material fatigue testing. The investigation results show that the developed adaptive predictor is a reliable and robust forecasting tool. It can capture the system's dynamic behaviour quickly and track the system's characteristics accurately. Its performance is superior to other classical forecasting schemes.
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