Hessian matrix estimation in hybrid systems based on an embedded FFNN

Seung Mook Baek, Jung Wook Park

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


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
Issue number10
Publication statusPublished - 2010 Oct

Bibliographical note

Funding Information:
Manuscript received June 02, 2007; revised June 22, 2009 and December 18, 2009; accepted December 28, 2009. Date of publication March 15, 2010; date of current version September 01, 2010. This work was supported by the Manpower Development Program for Energy & Resources of MKE with Yonsei Electric Power Research Center (YEPRC) at Yonsei University, Seoul, Korea.

All Science Journal Classification (ASJC) codes

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


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