ISBN
Formato digital
979-13-87837-54-9
Fecha de publicación
06-10-2025
Licencia
D. R. © Copyright 2025. Alma Y. Alanis, Jorge Galvez, Omar Avalos, Eduardo Méndez-Palos, Jorge D. Rios, Adriana Peña Perez-Negron & Gabriel Martínez Soltero
Todos los contenidos de esta obra se comparten bajo la licencia Creative Commons Atri-bución/Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0). Esto implica que no está autorizado el uso comercial de la obra original ni de las eventuales obras derivadas, las cuales deberán distribuirse bajo la misma licencia que rige la obra original. No obstante, se permite a terceros compartir el contenido siempre y cuando se reconozca debidamente la autoría y la publicación original en esta editorial.
Brandon Ismael Ayala Aguiñaga
Universidad de Guadalajara
0009-0004-7619-3897
Sofía A. González Robles
Universidad de Guadalajara
Luis Carlos Zayas Hernández
Universidad de Guadalajara
Hugo G. Venegas
Universidad de Guadalajara
0000-0002-4522-4097
Ricardo E. García Manzo
Universidad de Guadalajara
0000-0002-1210-3459
Acerca de
Nonlinear system identification is essential for modeling complex dynamics where analytical approaches fail. Recurrent high-order neural networks (RHONNs) excel in this task but require robust training methods. Traditional approaches like the Extended Kalman Filter (EKF) struggle with non-Gaussian noise and local linearization constraints. This paper proposes Particle Filter (PF) based training for RHONNs, enabling flexible and accurate state estimation. Simulations on a pendulum and a Duffing oscillator show that PF significantly reduces identification errors compared to EKF, especially under non-Gaussian noise. The results confirm PF’s potential for robust and adaptive neural training, enhancing nonlinear system identification for applications in control and robotics.
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