Approximation of large-scale dynamical systems for Benchmark Collection

In this contribution, We present a benchmark collection Inclusive of some needful real-world examples, which can be used to assessment and compare numerical methods for model reduction. In this paper the reduction method is explored for getting structure preserving reduced order model of a large-scale dynamical system, we have considered model order reduction of higher order LTI systems) with SISO and MIMO [XXXII] that aims at finding Error estimation using Approximation of both system. This enables a new evaluation of the error system Provided that the Observability Gramian of the original system has once been considered, an H-infinity and H-2 error bound can be computed with negligible numerical attempt for any reduced model attributable to The reduced order model (ROM) of a large-scale dynamical system is necessary to effortlessness the analysis of the system using approximation Algorithms. The response evaluation is considered in terms of response constraints and graphical assessments. the application of Approximation methods is offered for arising ROM of the large-scale LTI systems which includes benchmark problems. It is reported that the reduced order model using compare numerical methods is almost alike in performance to that of with original systems. all simulation results have been obtained via MATLAB based software (sssMOR toolbox).