Fast nonlinear model order reduction via associated transforms of high-order volterra transfer functions

Authors:
Yang Zhang;Haotian Liu;Qing Wang;Neric Fong;Ngai Wong
Affiliations:
The University of Hong Kong, Hong Kong;The University of Hong Kong, Hong Kong;The University of Hong Kong, Hong Kong;The University of Hong Kong, Hong Kong;The University of Hong Kong, Hong Kong
Venue:
Proceedings of the 49th Annual Design Automation Conference
Year:
2012

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Abstract

We present a new and fast way of computing the projection matrices serving high-order Volterra transfer functions in the context of (weakly and strongly) nonlinear model order reduction. The novelty is to perform, for the first time, the association of multivariate (Laplace) variables in high-order multiple-input multiple-output (MIMO) transfer functions to generate the standard single-s transfer functions. The consequence is obvious: instead of finding projection subspaces about every si, only that about a single s is required. This translates into drastic saving in computation and memory, and much more compact reduced-order nonlinear models, without compromising any accuracy.