Exact Search Directions for Optimization of Linear and Nonlinear Models Based on Generalized Orthonormal Functions


Autoria(s): ROSA, Alex da; CAMPELLO, Ricardo J. G. B.; AMARAL, Wagner C.
Contribuinte(s)

UNIVERSIDADE DE SÃO PAULO

Data(s)

20/10/2012

20/10/2012

2009

Resumo

A novel technique for selecting the poles of orthonormal basis functions (OBF) in Volterra models of any order is presented. It is well-known that the usual large number of parameters required to describe the Volterra kernels can be significantly reduced by representing each kernel using an appropriate basis of orthonormal functions. Such a representation results in the so-called OBF Volterra model, which has a Wiener structure consisting of a linear dynamic generated by the orthonormal basis followed by a nonlinear static mapping given by the Volterra polynomial series. Aiming at optimizing the poles that fully parameterize the orthonormal bases, the exact gradients of the outputs of the orthonormal filters with respect to their poles are computed analytically by using a back-propagation-through-time technique. The expressions relative to the Kautz basis and to generalized orthonormal bases of functions (GOBF) are addressed; the ones related to the Laguerre basis follow straightforwardly as a particular case. The main innovation here is that the dynamic nature of the OBF filters is fully considered in the gradient computations. These gradients provide exact search directions for optimizing the poles of a given orthonormal basis. Such search directions can, in turn, be used as part of an optimization procedure to locate the minimum of a cost-function that takes into account the error of estimation of the system output. The Levenberg-Marquardt algorithm is adopted here as the optimization procedure. Unlike previous related work, the proposed approach relies solely on input-output data measured from the system to be modeled, i.e., no information about the Volterra kernels is required. Examples are presented to illustrate the application of this approach to the modeling of dynamic systems, including a real magnetic levitation system with nonlinear oscillatory behavior.

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Brazilian National Council for Scientific and Technological Development (CNPq)[140706/2005-4]

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Brazilian National Council for Scientific and Technological Development (CNPq)[306229/2006-4]

Brazilian National Council for Scientific and Technological Development (CNPq)[301063/2007-9]

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Research Foundation of the State of Sao Paulo (Fapesp)[06/50231-5]

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Identificador

IEEE TRANSACTIONS ON AUTOMATIC CONTROL, v.54, n.12, p.2757-2772, 2009

0018-9286

http://producao.usp.br/handle/BDPI/28768

10.1109/TAC.2009.2031721

http://dx.doi.org/10.1109/TAC.2009.2031721

Idioma(s)

eng

Publicador

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

Relação

Ieee Transactions on Automatic Control

Direitos

restrictedAccess

Copyright IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

Palavras-Chave #Back-propagation-through-time technique #generalized orthonormal bases of functions (GOBF) #Kautz #Laguerre #linear and nonlinear systems identification #optimization #orthonormal basis functions (OBF) #Volterra series #SYSTEM-IDENTIFICATION #OPTIMALITY CONDITIONS #LAGUERRE FUNCTIONS #DYNAMICAL-SYSTEMS #OPTIMAL EXPANSION #KAUTZ FUNCTIONS #TIME-SCALE #FILTERS #CHOICE #NETWORKS #Automation & Control Systems #Engineering, Electrical & Electronic
Tipo

article

original article

publishedVersion