High-Order Sliding Modes Based On-Line Training Algorithm for Recurrent High-Order Neural Networks. Issue 2 (2020) - Alanis, Alma Y.; Rios-Huerta, Daniel; Rios, Jorge D.; Arana-Daniel, Nancy; Lopez-Franco, Carlos; Sanchez, Edgar N.
Abstract: This work presents a discrete on-line training algorithm for recurrent high-order neural networks (RHONN). The proposed training algorithm is based on the arbitrary order differentiators of high-order sliding modes (HOSM) theory. Due to HOSM-based differentiators can approximate derivatives in finite time, the proposed training algorithm avoids the compute of the derivatives, unlike conventional training algorithms. The proposed HOSM-based algorithm is implemented for the training of a RHONN identifier, and its performance is compared with the results using the extended Kalman filter (EKF) training algorithm. Results of a implementation of the identifier for the Lorenz system and an implementation of the identifier for a tracked robot using experimental data are presented.