New approach of Gerschgorin theorem in model order reduction

2015 ◽  
Vol 35 (3-4) ◽  
pp. 143-149 ◽  
Author(s):  
Yogesh V. Hote ◽  
Amar Nath Jha
Author(s):  
Jérôme Guillet ◽  
Benjamin Mourllion ◽  
Abderazik Birouche ◽  
Michel Basset

Extracting second-order structures from single-input state-space models: Application to model order reductionThis paper focuses on the model order reduction problem of second-order form models. The aim is to provide a reduction procedure which guarantees the preservation of the physical structural conditions of second-order form models. To solve this problem, a new approach has been developed to transform a second-order form model from a state-space realization which ensures the preservation of the structural conditions. This new approach is designed for controllable single-input state-space realizations with real matrices and has been applied to reduce a single-input second-order form model by balanced truncation and modal truncation.


2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Othman M. K. Alsmadi ◽  
Zaer S. Abo-Hammour

A robust computational technique for model order reduction (MOR) of multi-time-scale discrete systems (single input single output (SISO) and multi-input multioutput (MIMO)) is presented in this paper. This work is motivated by the singular perturbation of multi-time-scale systems where some specific dynamics may not have significant influence on the overall system behavior. The new approach is proposed using genetic algorithms (GA) with the advantage of obtaining a reduced order model, maintaining the exact dominant dynamics in the reduced order, and minimizing the steady state error. The reduction process is performed by obtaining an upper triangular transformed matrix of the system state matrix defined in state space representation along with the elements ofB,C, andDmatrices. The GA computational procedure is based on maximizing the fitness function corresponding to the response deviation between the full and reduced order models. The proposed computational intelligence MOR method is compared to recently published work on MOR techniques where simulation results show the potential and advantages of the new approach.


2017 ◽  
Vol 33 (3) ◽  
pp. 857-874 ◽  
Author(s):  
Keith Porter ◽  
Edward Field ◽  
Kevin Milner

The size of the logic tree within the Uniform California Earthquake Rupture Forecast Version 3, Time-Dependent (UCERF3-TD) model can challenge risk analyses of large portfolios. An insurer or catastrophe risk modeler concerned with losses to a California portfolio might have to evaluate a portfolio 57,600 times to estimate risk in light of the hazard possibility space. Which branches of the logic tree matter most, and which can one ignore? We employed two model-order-reduction techniques to simplify the model. We sought a subset of parameters that must vary, and the specific fixed values for the remaining parameters, to produce approximately the same loss distribution as the original model. The techniques are (1) a tornado-diagram approach we employed previously for UCERF2, and (2) an apparently novel probabilistic sensitivity approach that seems better suited to functions of nominal random variables. The new approach produces a reduced-order model with only 60 of the original 57,600 leaves. One can use the results to reduce computational effort in loss analyses by orders of magnitude.


10.29007/gv5q ◽  
2018 ◽  
Author(s):  
Stanley Bak

Tool Presentation: We evaluate an improved reachability algorithm for linear (and affine) systems implemented in the continuous branch of the Hylaa tool. While Hylaa’s earlier approach required n simulations to verify an n-dimensional system, the new method takes advantage of additional problem structure to produce the same verification result in significantly less time. If the initial states can be defined in i dimensions, and the output variables related to the property being checked are o-dimensional, the new approach needs only min(i,o) simulations to verify the system, or produce a counter-example. In addition to reducing the number of simulations, a second improvement speeds up individual simulations when the dynamics is sparse by using Krylov subspace methods.At ARCH 2017, we used the original approach to verify nine large linear benchmarks taken from model order reduction. Here, we run the new algorithm on the same set of benchmarks, and get an identical verification result in a fraction of the time. None of the benchmarks need more than tens of seconds to complete. The largest system with 10922 dimensions, which took over 24 hours using last year’s method, is verified in 3.4 seconds.


Author(s):  
Vladimir Lantsov ◽  
A. Papulina

The new algorithm of solving harmonic balance equations which used in electronic CAD systems is presented. The new algorithm is based on implementation to harmonic balance equations the ideas of model order reduction methods. This algorithm allows significantly reduce the size of memory for storing of model equations and reduce of computational costs.


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