IIIE 20352
INSTITUTO DE INVESTIGACIONES EN INGENIERIA ELECTRICA "ALFREDO DESAGES"
Unidad Ejecutora - UE
artículos
Título:
Volterra-type models for nonlinear systems identification
Autor/es:
CHRISTIAN SCHMIDT; SILVINA INES BIAGIOLA; JUAN EDMUNDO COUSSEAU; JOSE LUIS FIGUEROA
Revista:
APPLIED MATHEMATICAL MODELLING
Editorial:
ELSEVIER SCIENCE INC
Referencias:
Lugar: Amsterdam; Año: 2014 vol. 38 p. 2414 - 2421
ISSN:
0307-904X
Resumen:
In this work, multi-input multi-output (MIMO) nonlinear process identification is dealt with. In particular, two Volterra-type models are discussed in the context of system identification. These models are: Memory Polynomial (MP) and Modified Generalized Memory Polynomial (MGMP), which can be considered as a generalization of Hammerstein and Wiener models, respectively. Both of them are appealing representations as they allow to describe larger model sets with less parametric complexity. Simulation example is given to illustrate the quality of the obtained models.
