In this study, a new technique for discrete time system reduction is suggested which preserves the substructure of the higher order system in the reduced system. Motivated by various system reduction and optimization techniques available in the literature, the proposed technique is based on Cuckoo search which is used to obtain unknown elements of the reduced system with an error criterion minimization. The efﬁcacy of the proposed technique is justiﬁed by reducing few benchmark systems and the obtained results are compared with other well-known order reduction methods existing in the literature. 1 Introduction bilinear, linear transformation, etc. (Chu and Glover 1999; Shih and WD 1973). Finally the reduced order model is Reduced order modelling has become a signiﬁcant area of achieved using corresponding inverse transformation. In research in systems engineering since 1960s. Numerous the recent years, artiﬁcial neural network (ANN) (Alsmadi techniques are developed for continuous and discrete time et al. 2011), genetic algorithm (GA) (Alsmadi and Abo- systems (Moore 1981; Aoki 1968; Shamash 1974; Hutton Hammour 2015), step response matching (SRM) and Friedland 1975; Obinata and Inooka 1983; El-Attar (Mukherjee et al. 2007) and differential evolution (DE) and Vidyasagar 1978; Bistritz 1982; Hwang et al. 1983; (Namratha and
Microsystem Technologies – Springer Journals
Published: Jun 4, 2018
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