Hessian matrix estimation in hybrid systems based on an embedded FFNN

Seung Mook Baek, Jung Wook Park

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

This paper describes the Hessian matrix estimation of nonsmooth nonlinear parameters by the identifier based on a feedforward neural network (FFNN) embedded in a hybrid system, which is modeled by the differentialalgebraicimpulsiveswitched (DAIS) structure. After identifying full dynamics of the hybrid system, the FFNN is used to estimate second-order derivatives of an objective function J with respect to the nonlinear parameters from the gradient information, which are trajectory sensitivities. Then, the estimated Hessian matrix is applied to the optimal tuning of a saturation limiter used in a practical engineering system.

Original languageEnglish
Article number5431074
Pages (from-to)1533-1542
Number of pages10
JournalIEEE Transactions on Neural Networks
Volume21
Issue number10
DOIs
Publication statusPublished - 2010 Oct 1

Fingerprint

Feedforward neural networks
Hybrid systems
Limiters
Systems engineering
Tuning
Trajectories
Derivatives

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Software
  • Medicine(all)

Cite this

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Hessian matrix estimation in hybrid systems based on an embedded FFNN. / Baek, Seung Mook; Park, Jung Wook.

In: IEEE Transactions on Neural Networks, Vol. 21, No. 10, 5431074, 01.10.2010, p. 1533-1542.

Research output: Contribution to journalArticle

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