Hessian matrix estimation in hybrid systems based on an embedded FFNN

  • Authors:

  • Seung-Mook Baek;Jung-Wook Park

  • Affiliations:

  • School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea;School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea

  • Venue:
  • IEEE Transactions on Neural Networks
  • Year:
  • 2010

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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 differential-algebraic-impulsive-switched (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.