Abstract
In this study, we propose the least disturbance algorithm adding scale factor and shift factor. The dynamic learning ratio can be calculated to minimize the scale factor and shift factor of wavelet function and the variation of net weights and the algorithm improve the stability and the convergence of wavelet neural network. It was applied to build the dynamical model of autonomous underwater vehicles and the residuals are generated by comparing the outputs of the dynamical model with the real state values in the condition of thruster fault. Fault detection rules are distilled by residual analysis to execute thruster fault diagnosis. The results of simulation prove the effectiveness.
Highlights
With the development of the activities in deep ocean, the application of underwater vehicles is widespread (Xu and Xiao, 2007; Blidberg, 1991)
We propose a least disturbance wavelet neural network to build up the dynamic model of underwater vehicles and add scale factor and shift factor of wavelet function to dynamic learning rate algorithm based on steepest descent method
We propose the least disturbance algorithm adding scale factor and shift factor
Summary
With the development of the activities in deep ocean, the application of underwater vehicles is widespread (Xu and Xiao, 2007; Blidberg, 1991). Neural network has the characters of strong input-output nonlinear mapping, distributed store of information, parallel process and especially strong self-organizing and selflearning ability, which make neural network become an effective method for fault diagnosis. It has been applied in practice (Alessandri et al, 1999). We propose a least disturbance wavelet neural network to build up the dynamic model of underwater vehicles and add scale factor and shift factor of wavelet function to dynamic learning rate algorithm based on steepest descent method.
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