Asymmetric volterra models based on ladder-structured generalized orthonormal basis functions

Machado, Jeremias B., Campello, Ricardo J.G.B., and Amaral, Wagner C. (2015) Asymmetric volterra models based on ladder-structured generalized orthonormal basis functions. IEEE Transactions on Automatic Control, 60 (11). pp. 2879-2891.

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Abstract

In this paper, an improved method to construct and estimate Volterra models using Generalized Orthonormal Basis Functions (GOBF) is presented. The proposed method extends results obtained in previous works, where an exact technique for optimizing the GOBF parameters (poles) for symmetric Volterra models of any order was presented. The proposed extensions take place in two different ways: (i) the new formulation is derived in such a way that each multidimensional kernel of the model is decomposed into a set of independent orthonormal bases (rather than a single, common basis), each of which is parameterized by an individual set of poles responsible for representing the dominant dynamic of the kernel along a particular dimension; and (ii) the new formulation is based on a ladder-structured GOBF architecture that is characterized by having only real-valued parameters to be estimated, regardless of whether the GOBF poles encoded by these parameters are real-or complex-valued. The exact gradients of an error functional with respect to the parameters to be optimized are computed analytically and provide exact search directions for an optimization process that uses only input-output data measured from the dynamic system to be modeled. Computational experiments are presented to illustrate the benefits of the proposed approach when modeling nonlinear systems.

Item ID: 47067
Item Type: Article (Research - C1)
ISSN: 1558-2523
Keywords: generalized orthonormal basis functions (GOBF), nonlinear systems, system identification, volterra models
Date Deposited: 04 Jan 2017 08:04
FoR Codes: 01 MATHEMATICAL SCIENCES > 0102 Applied Mathematics > 010299 Applied Mathematics not elsewhere classified @ 100%
SEO Codes: 97 EXPANDING KNOWLEDGE > 970101 Expanding Knowledge in the Mathematical Sciences @ 100%
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