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MIMO Volterra kernel recovery in the frequency domain using neural networks
O H L Preston1, T J Rogers1, K Worden1
1Dynamics Research Group, School of Mechanical, Aerospace and Civil Engineering, University of Sheffield, Mappin Street, Sheffield, S1 3JD UK.
Abstract:
This paper presents a detailed analysis of a process for recovering multi-degree-of-freedom Volterra kernels using neural network weights, addressing the fact that such Higher-order Frequency Response Functions (HFRFs) have not previously been directly recovered from data. A novel method is proposed for HFRF recovery of Volterra kernels up to third order, and its effectiveness is demonstrated on a variety of systems using simulated data for validation. The harmonic-probing algorithms are derived from a general multi-input-multi-output NARX neural network model. These algorithms recover Volterra kernels for time-invariant systems with up to n degrees of freedom. The results demonstrate the accuracy of the method and suggest a promising direction for HFRF recovery in nonlinear time-invariant systems.
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