Reducibility matrix based model reduction via recurrent neural network tuning

Authors:
Othman Alsmadi;Musa Abdalla
Affiliations:
University of Jordan, Amman - Jordan;University of Jordan, Amman - Jordan
Venue:
MIC '07 Proceedings of the 26th IASTED International Conference on Modelling, Identification, and Control
Year:
2007

Citing 6
Cited 0

Matrix analysis

Matrix analysis
Matrix analysis and applied linear algebra

Matrix analysis and applied linear algebra
Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation
A Unified Algebric Approach to Control Design

A Unified Algebric Approach to Control Design
Sylvester equations and projection-based model reduction

Journal of Computational and Applied Mathematics - Special issue: Proceedings of the international conference on linear algebra and arithmetic, Rabat, Morocco, 28-31 May 2001
Model Reduction of MIMO Systems via Tangential Interpolation

SIAM Journal on Matrix Analysis and Applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

A new order model reduction technique is presented in this paper. The reduction technique is based on a matrix reducibility concept, which incorporates a recurrent neural network algorithm for parameter tuning. The eigenvalues of the reduced order model are selected as a subset of the full order model eigenvalues. A more physically meaningful representation of the reduced order model is achieved by maintaining or preserving the full order system's slow modes eigenvalues. Finally, simulation of a numerical example and a comparison with the singular perturbation model reduction method is provided in this paper.