A new online hybrid learning algorithm of adaptive neural fuzzy inference system for fault prediction
by Zhanlong Du; Xiaomin Li; Qiong Mao
International Journal of Modelling, Identification and Control (IJMIC), Vol. 23, No. 1, 2015

Abstract: Effective prognostic tools are crucial for maintenance to predict faults before systems are completely damaged and to ensure reliability. A new online hybrid learning method for adaptive neural fuzzy inference system (ANFIS) prediction model is presented in this paper based on square-root cubature Kalman filter (SCKF) and recursive least squares (RLS). SCKF and RLS are used to recursively optimise ANFIS nonlinear and linear parameters, respectively. Fault feature sensitive to system degradation process is selected as ANFIS predicting variable for fault prediction. The simulation results indicate that the higher forecasting accuracy or the lower computational complexity is obtained compared with other three online learning methods.

Online publication date: Tue, 31-Mar-2015

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