Hessian matrix estimation in hybrid systems based on an embedded FFNN

Seung Mook Baek, Jung Wook Park

Research output: Contribution to journalArticle

4 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

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

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