Model order reduction using Fuzzy C-Means clustering

Abstract

In this paper, a mixed method of model order reduction for a continuous-time single-input single-output system is presented. The denominator of a reduced-order model (ROM) is obtained by clustering the poles of the original high-order system using the Fuzzy C-Means clustering technique retaining some dominant poles. Having determined the denominator polynomial, numerator coefficients are found by Padé approximation by matching the desired number of time moments and Markov parameters. The ROM of the proposed method provides good approximation to the original system both in terms of transient and steady-state response.