Hessian Matrix Estimation in Hybrid Systems Based on an Embedded FFNN

Author

Baek, Seung-Mook ; Park, Jung-Wook

Author_Institution

Sch. of Electr. & Electron. Eng., Yonsei Univ., Seoul, South Korea

Volume

21

Issue

10

fYear

2010

Firstpage

1533

Lastpage

1542

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.

Keywords

Hessian matrices; differential algebraic equations; feedforward neural nets; gradient methods; nonlinear estimation; parameter estimation; time-varying systems; Hessian matrix estimation; differential-algebraic-impulsive-switched structure; embedded FFNN; feedforward neural network; gradient information; hybrid system; nonsmooth nonlinear parameters; optimal tuning; saturation limiter; second-order derivative; trajectory sensitivity; Eigenvalues and eigenfunctions; Hybrid power systems; Neural networks; Nonlinear dynamical systems; Optimization methods; Power engineering and energy; Power system analysis computing; Power system dynamics; Power system modeling; Tuning; Feedforward neural network (FFNN); Hessian matrix estimation; hybrid system; nonlinear parameters; optimal tuning; power system stabilizer (PSS); saturation limiter; Algorithms; Neural Networks (Computer); Nonlinear Dynamics;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/TNN.2010.2042728

Filename

5431074