Model order reduction using factor division algorithm and fuzzy c-means clustering technique

This paper proposes a novel hybrid approach that combines factor division algorithm and fuzzy c-means clustering technique for reducing the model order of high-order linear time invariant system. 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 numerator of the higher order model is reduced using the factor division algorithm and the denominator of the higher order model is reduced using the fuzzy c-means clustering technique. The stability of the model is also verified using the pole zero stability analysis and it was found that the obtained reduced order model (ROM) is stable. Further, the steady state and transient response of the ROM is found to be better than the other existing techniques. The performance of the ROM is compared to other existing techniques in terms of integral square error, integral of time multiply squared error, integral absolute error and integral time-weighted absolute error.