Optimized feedforward neural networks. for on-line identification of nonlinear models

被引：0

作者：

Alessandri, A ^{[1
]}

Sanguineti, M ^{[1
]}

Maggiore, M ^{[1
]}

机构：

[1] Natl Res Council Italy, CNR, ISSIA, Inst Intelligent Syst Automat, I-16149 Genoa, Italy

来源：

PROCEEDINGS OF THE 41ST IEEE CONFERENCE ON DECISION AND CONTROL, VOLS 1-4 | 2002年

关键词：

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：1751 / 1756

页数：6

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