System reduction using factor division algorithm and eigen spectrum analysis

Parmar, Girish; Mukherjee, S.; Prasad, Rajendra

doi:10.1016/j.apm.2006.10.004

Cited by 124 publications

(39 citation statements)

References 24 publications

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“…This minimization leads to a non-convex problem that can get stuck at local minima and hence optimality will not be achieved [15]. Recently, Parmar et al [16] proposed a reduction method with pole centroid retaining in the reduced model. Their method deals with SISO systems only.…”

Section: Introductionmentioning

confidence: 97%

Genetic algorithm approach with frequency selectivity for model order reduction of MIMO systems

Alsmadi

Abo-Hammour

Al-Smadi

et al. 2011

Mathematical and Computer Modelling of Dynamical Systems

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A novel genetic algorithm (GA) approach with frequency selectivity advantage for model order reduction (MOR) of multi-input-multi-output (MIMO) systems is presented in this article. Motivated by singular perturbation and other reduction techniques, the new MOR method is formulated using GAs, which can be applied to single-input-single-output (SISO)-or MIMO-type systems. The GA procedure is based on maximizing the fitness function corresponding to the response deviation between the full-order model and the reduced-order model with the option of substructure preservation. The proposed GA-MOR method is compared to the well-known reduction techniques, such as the Schur decomposition balanced truncation, proper orthogonal decomposition (POD) and state elimination through balancing-related frequency-weighted realization in addition to other recent methods. Simulation results validate the superiority and robustness of the new MOR technique as it can search the solution space for almost optimal solutions.

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Section: Introductionmentioning

confidence: 97%

Genetic algorithm approach with frequency selectivity for model order reduction of MIMO systems

Alsmadi

Abo-Hammour

Al-Smadi

et al. 2011

Mathematical and Computer Modelling of Dynamical Systems

View full text Add to dashboard Cite

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“…Further, several methods have also been suggested by combining the features of two deferent methods [5]. This technique is based onto combined factor division algorithm to obtain the zeroes of the reduced order model, and eigen spectrum analysis to obtain its poles [7]. Given the transfer function of the high-order n system (HOS), To construct ( ) s G r , the following procedure is proposed.…”

Section: Factor Division Algorithm and Eigen Spectrum Analysismentioning

confidence: 99%

Performances of Model Reduction using Factor Division Algorithm and Eigen Spectrum Analysis

Adamou‐Mitiche¹

2012

IJCTCM

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The easy and simplicity of manipulation and the multiple applications provided by approximate reducedorder systems, in comparison with original high order systems, make the topic of model reduction a subject with big importance; it allows us to get models that approached in their behavior, by satisfactory way, the high order original systems. In this paper we study one of recent reduction methods, which combine the algorithm of factor division and eigen spectrum analysis, and we compare it in the time domain (Schur method), and in the frequency domain (Routh approximation approach), in order to determine its performances.

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“…Saraswathi et al [17] proposed a method retaining some of the properties of original system based on eigenspectrum. The method can be applied for systems having both real and complex poles unlike the other existing methods [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

“…The poles of the reduced model are evenly spaced between the first and last poles. Parmar et al [10] proposed a mixed method using Eigen Spectrum Analysis with Factor division algorithm to determine the numerator of the reduced model with known denominator. Parmar et al [11] proposed another mixed method using Eigen Spectrum Analysis equation and Particle Swarm method to find the reduced model.…”

Section: Introductionmentioning

confidence: 99%

A Modified Method for Order Reduction of Large Scale Discrete Systems

Saraswathi¹

2011

IJACSA

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Abstract-In this paper, an effective procedure to determine the reduced order model of higher order linear time invariant discrete systems is discussed. A new procedure has been proposed for evaluating Time moments of the original high order system. Numerator and denominator polynomials of reduced order model are obtained by considering first few redefined time moments of the original high order system. The proposed method has been verified using numerical examples.

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System reduction using factor division algorithm and eigen spectrum analysis

Cited by 124 publications

References 24 publications

Genetic algorithm approach with frequency selectivity for model order reduction of MIMO systems

Genetic algorithm approach with frequency selectivity for model order reduction of MIMO systems

Performances of Model Reduction using Factor Division Algorithm and Eigen Spectrum Analysis

A Modified Method for Order Reduction of Large Scale Discrete Systems

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