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Updated: Apr 1, 2026

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials
Published on: May 25, 2019
Neural networks for tracking of unknown SISO discrete-time nonlinear dynamic systems
Muhammad Saleheen Aftab1, Muhammad Shafiq1
1Department of Electrical & Computer Engineering, Sultan Qaboos University, Muscat, Oman.
Abstract:
This article presents a Lyapunov function based neural network tracking (LNT) strategy for single-input, single-output (SISO) discrete-time nonlinear dynamic systems. The proposed LNT architecture is composed of two feedforward neural networks operating as controller and estimator. A Lyapunov function based back propagation learning algorithm is used for online adjustment of the controller and estimator parameters. The controller and estimator error convergence and closed-loop system stability analysis is performed by Lyapunov stability theory. Moreover, two simulation examples and one real-time experiment are investigated as case studies. The achieved results successfully validate the controller performance.
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