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

  • Nitin Singh
  • Niraj K. Choudhary
  • Rudar K. Gautam
  • Shailesh Tiwari
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 670)

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.

Keywords

Model order reduction Fuzzy c-means clustering Particle swarm optimization Pole clustering Fuzzy logic 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Nitin Singh
    • 1
  • Niraj K. Choudhary
    • 1
  • Rudar K. Gautam
    • 1
  • Shailesh Tiwari
    • 2
  1. 1.Department of Electrical EngineeringMNNIT AllahabadAllahabadIndia
  2. 2.ABES Engineering CollegeGhaziabadIndia

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