TY - GEN
T1 - Adaptive linearization for nonlinear systems using continuous neural networks
AU - Escudero, Marisol
AU - Chairez, Isaac
AU - García, Alejandro
PY - 2010
Y1 - 2010
N2 - The adaptive linearization of dynamic nonlinear systems remains as an open problem due to the complexities associated with the methods required to obtain the linearized sections. This problem is even more difficult if the system is uncertain, it means, if only partial or null information about the mathematical model of the system is available. This paper presents a proposal of an adaptive linearization method for uncertain nonlinear systems affected by additive perturbations by the Artificial Neural Networks approach. The stability of the identification error is formally boarded and proved by the second Lyapunov's method. Such suggested structure preserves some inherited structural properties that allows this method to behave as the original model as is exposed. A comparison of the developed algorithm with a similar structure without adaptable linear term is carried out, considering a genetic regulation mathematical model. The results of the simulation show that this proposal presents a superior performance as is observed in the trajectories of each identifier and by comparing the performance index of each one.
AB - The adaptive linearization of dynamic nonlinear systems remains as an open problem due to the complexities associated with the methods required to obtain the linearized sections. This problem is even more difficult if the system is uncertain, it means, if only partial or null information about the mathematical model of the system is available. This paper presents a proposal of an adaptive linearization method for uncertain nonlinear systems affected by additive perturbations by the Artificial Neural Networks approach. The stability of the identification error is formally boarded and proved by the second Lyapunov's method. Such suggested structure preserves some inherited structural properties that allows this method to behave as the original model as is exposed. A comparison of the developed algorithm with a similar structure without adaptable linear term is carried out, considering a genetic regulation mathematical model. The results of the simulation show that this proposal presents a superior performance as is observed in the trajectories of each identifier and by comparing the performance index of each one.
KW - Adaptive linearization
KW - Continuous neural networks
KW - Gene regulation system
KW - Identification
UR - http://www.scopus.com/inward/record.url?scp=78650259616&partnerID=8YFLogxK
U2 - 10.1109/ICEEE.2010.5608672
DO - 10.1109/ICEEE.2010.5608672
M3 - Contribución a la conferencia
AN - SCOPUS:78650259616
SN - 9781424473120
T3 - Program and Abstract Book - 2010 7th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2010
SP - 116
EP - 121
BT - Program and Abstract Book - 2010 7th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2010
T2 - 2010 7th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2010
Y2 - 8 September 2010 through 10 September 2010
ER -