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.
Dariana Gómez Álvarez
Universidad de Guadalajara
0009-0002-7286-2083
Michel López Franco
Universidad de Guadalajara
0000-0002-4245-9683
Alma Yolanda Alanis García
Universidad de Guadalajara
0000-0001-9600-779X
Carlos López Franco
Universidad de Guadalajara
0000-0001-8122-3799
Jesús Hernández Barragan
Universidad de Guadalajara
0000-0001-7518-1668
Edgar N. Sánchez
Universidad de Guadalajara
0000-0002-8695-7879
Acerca de
Nonlinear system identification is essential for modeling com- plex 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 con- firm PF’s potential for robust and adaptive neural training, enhancing nonlinear system identification for applications in control and robotics.
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