Neural identifier for unknown discrete-time nonlinear delayed systems. Issue 8 (November 2016)

• Record Type: Journal Article
• Authors: Alanis, Alma; Rios, Jorge; Arana-Daniel, Nancy; Lopez-Franco, Carlos
• Is Part Of: Neural computing & applications. Volume 27:Issue 8(2016)
• Page Start: 2453
• Page End: 2464
• Publication Date: 2016-11
• Subjects: Recurrent high-order neural networks -- Neural identification -- MIMO systems -- Unknown time-delay -- Lyapunov -- Discrete-time nonlinear systems; Neural networks (Computer science) -- Periodicals; Neural circuitry -- Periodicals; Artificial intelligence -- Periodicals; Neural Networks (Computer) -- Periodicals; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux nerveux -- Périodiques; Intelligence artificielle -- Périodiques; 006.32
• DOI: 10.1007/s00521-015-2016-7
• Languages: English
• ISSNs: 0941-0643

Abstract

Abstract This work proposes a discrete-time nonlinear neural identifier based on a recurrent high-order neural network trained with an extended Kalman filter-based algorithm for discrete-time deterministic multiple-input multiple-output systems with unknown dynamics and time-delay. To prove the semi-globally uniformly ultimately boundedness of the proposed neural identifier, the stability analysis based on the Lyapunov approach is included. Applicability of the proposed identifier is shown via simulation and experimental results, all of them performed under the presence of unknown external and internal disturbances as well as unknown time-delays.