Paper in Neuroprose: Nonlinear System Identification Using Additive Dynamic Neural Networks

[Robert Grino]

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NONLINEAR SYSTEM IDENTIFICATION USING ADDITIVE DYNAMIC NEURAL NETWORKS
R. Grino (grino at ic.upc.es)
Instituto de Cibernetica - ESAII
Univ. Politecnica Catalunya
Diagonal, 647, 2nd floor
08028-Barcelona, SPAIN
(Reprint of a SICICA'94 (IFAC) paper)


ABSTRACT:
In this work additive dynamic neural models are used for the 
identification of nonlinear plants in on-line operation. In order to accomplish
this task a gradient parameter adaptation method based in sensitivity 
analysis is formulated taking into account that the parameters of the model 
are arranged in matrix form. This methodology is applied to several nonlinear 
systems in simulation and with a real dataset to verify its performance.

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   Robert Grino                   E-mail: grino at ic.upc.es
   Instituto de Cibernetica               
   Diagonal,647, 2nd floor        FAX number: (343) 4016605
   08028 - Barcelona              
       SPAIN    
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