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Neural Computing and Applications

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22-03-2018 | Original Article

A novel hybrid metaheuristic algorithm for model order reduction in the delta domain: a unified approach

Authors: Souvik Ganguli, Gagandeep Kaur, Prasanta Sarkar

Published in: Neural Computing and Applications | Issue 10/2019

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Abstract

Delta operator parameterization provides a unified framework in modeling, analysis and design of discrete-time systems, in which the resultant model converges to its continuous-time counterpart at high sampling limit. Capitalizing this unique property of delta operator, a new hybrid algorithm combining gray wolf optimizer and firefly algorithm has been proposed for model order reduction of high-dimensional linear discrete-time system. It has been shown that the reduced discrete-time model inherits all the dominant characteristics of the higher-order discrete-time model and with the increase in sampling frequency it converges to the continuous-time reduced model. The effectiveness of the proposed method is illustrated with the help of an example.

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Title: A novel hybrid metaheuristic algorithm for model order reduction in the delta domain: a unified approach
Authors: Souvik Ganguli
Gagandeep Kaur
Prasanta Sarkar
Publication date: 22-03-2018
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 10/2019
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-018-3440-2

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