Hessian matrix estimation in hybrid systems based on an embedded FFNN.
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.
School of Electrical and Electronic Engineering, Yonsei University, Seoul 120-749, Korea. sm_baek@yonsei.ac.kr
Seung-Mook
SM
Baek
Jung-Wook
JW
Park
D000465:Algorithms; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics