On-line system identification using additive dynamic neural networks. An invariant imbedding approach

Author

Griñó, Robert

Author_Institution

Inst. de Cibernetica, Univ. Politecnica de Catalunya, Barcelona, Spain

fYear

1996

Firstpage

55

Lastpage

63

Abstract

In this work additive dynamic neural models are used for the identification of nonlinear plants in online operation. In order to accomplish this task an invariant imbedding method and matrix calculus has been applied to the variational solution of the parameter identification problem to obtain its online version. The work also includes a complexity study of the developed solution

Keywords

identification; matrix algebra; neural nets; variational techniques; additive dynamic neural networks; complexity study; invariant imbedding approach; invariant imbedding method; matrix calculus; nonlinear plants; online operation; online system identification; parameter identification problem; variational solution; Artificial neural networks; Biological system modeling; Delay lines; Feedforward neural networks; Multi-layer neural network; Neural networks; Neurons; Nonlinear dynamical systems; State-space methods; System identification;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks for Identification, Control, Robotics, and Signal/Image Processing, 1996. Proceedings., International Workshop on

Conference_Location

Venice

Print_ISBN

0-8186-7456-3

Type

conf

DOI

10.1109/NICRSP.1996.542745

Filename

542745