Title :
Optimized feedforward neural networks for on-line identification of nonlinear models
Author :
Alessandri, A. ; Sanguineti, M. ; Maggiore, M.
Author_Institution :
Inst. of Intelligent Syst. for Autom., Nat. Res. Council, Genova, Italy
Abstract :
Optimization of a class of nonlinear approximators corresponding to feedforward neural networks is investigated for on-line identification of nonlinear models in high-dimensional settings. The parameters are optimized by minimizing a cost function, which consists of the summation of two terms: a fitting penalty term and a term related to changes in the parameters. The relative influence of the two terms on the overall minimization can be tuned, according to a proper scalar. The resulting algorithm has properties of convergence and robustness. Simulation results are performed to compare its performance with classical algorithms, such as back-propagation and learning based on the extended Kalman filter, used for adjusting parameters in neural-network identification of nonlinear models. The advantages of the proposed approach are shown.
Keywords :
approximation theory; feedforward neural nets; identification; learning (artificial intelligence); nonlinear systems; optimisation; backpropagation; classical algorithms; convergence; extended Kalman filter; nonlinear approximators; nonlinear models; online identification; optimized feedforward neural networks; robustness; Automation; Backpropagation algorithms; Computer networks; Convergence; Cost function; Feedforward neural networks; Intelligent networks; Intelligent systems; Neural networks; Robustness;
Conference_Titel :
Decision and Control, 2002, Proceedings of the 41st IEEE Conference on
Print_ISBN :
0-7803-7516-5
DOI :
10.1109/CDC.2002.1184775