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Erschienen in:
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2019 | OriginalPaper | Buchkapitel

Model Order Reduction Using Fuzzy C-Means Clustering and Particle Swarm Optimization

verfasst von : Nitin Singh, Niraj K. Choudhary, Rudar K. Gautam, Shailesh Tiwari

Erschienen in: Smart Innovations in Communication and Computational Sciences

Verlag: Springer Singapore

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Abstract

The hybrid method which combines the evolutionary programming technique, i.e., based on the swarm optimization algorithm and fuzzy c-means clustering method is used for reducing the model order of high-order linear time-invariant systems in the presented work. The process of clustering is used for finding the group of objects with similar nature that can be differentiated from the other dissimilar objects. The reduction of the numerator of original high-order model is done using the particle swarm optimization algorithm, and fuzzy c-means clustering technique is used for reducing the denominator of the higher-order model. The stability of the model is also verified using the pole zero stability analysis, and it was found that the obtained reduced-order model is stable. Further, the transient and steady state response of the obtained lower-order model as compared to the other existing techniques are better. The output of the obtained lower-order model is also compared with the other existing techniques in the literature in terms of ISE, ITSE, IAE, and ITAE.

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Vorheriges Kapitel An Innovative Technique Toward the Recognition of Carcinoma Using Classification and Regression Technique
Nächstes Kapitel Control Chart Pattern Recognition Based on Convolution Neural Network
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Metadaten
Titel
Model Order Reduction Using Fuzzy C-Means Clustering and Particle Swarm Optimization
verfasst von
Nitin Singh
Niraj K. Choudhary
Rudar K. Gautam
Shailesh Tiwari
Copyright-Jahr
2019
Verlag
Springer Singapore
Buch
Smart Innovations in Communication and Computational Sciences

Print ISBN: 978-981-10-8970-1

Electronic ISBN: 978-981-10-8971-8

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-981-10-8971-8_8