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Hessian matrix estimation in hybrid systems based on an embedded FFNN
Seung-Mook Baek1, Jung-Wook Park
1School of Electrical and Electronic Engineering, Yonsei University, Seoul 120-749, Korea. sm_baek@yonsei.ac.kr
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.
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