Improved neural solution for the Lyapunov matrix equation based on gradient search

Improved neural solution for the Lyapunov matrix equation based on gradient search

0.00 Avg rating0 Votes
Article ID: iaor20141104
Volume: 113
Issue: 22-24
Start Page Number: 876
End Page Number: 881
Publication Date: Nov 2013
Journal: Information Processing Letters
Authors: , ,
Keywords: matrices, neural networks, simulation: applications
Abstract:

By using the hierarchical identification principle, based on the conventional gradient search, two neural subsystems are developed and investigated for the online solution of the well‐known Lyapunov matrix equation. Theoretical analysis shows that, by using any monotonically‐increasing odd activation function, the gradient‐based neural networks (GNN) can solve the Lyapunov equation exactly and efficiently. Computer simulation results confirm that the solution of the presented GNN models could globally converge to the solution of the Lyapunov matrix equation. Moreover, when using the power‐sigmoid activation functions, the GNN models have superior convergence when compared to linear models.

Reviews

Required fields are marked *. Your email address will not be published.