<article_id>JIC_V4_N3_RP5</article_id>
<title>Model Reduction of Continuous and Discrete Time Systems using Differentiation Method with Many Clustering Techniques</title>
<author>Maneesh Kumar Gupta</author>
<author>Awadhesh Kumar</author>
<journal>Journal on Instrumentation & Control Engineering</journal>
<issn>2321 – 1148</issn>
<volume>4</volume>
<issue>3</issue>
<first_page>27</first_page>
<last_page>33</last_page>
<keywords>Model Order Reduction, Differentiation Method, Pole Clustering, Modified Pole Clustering, Dominant Pole
Clustering, Fuzzy C-means Clustering, Stability Equation, Integral Square Error</keywords>
<abstract>This paper presents, many mixed methods for Model Order Reduction (MOR) of a continuous approach for Single Input Single Output (SISO) system. The reduced order numerator polynomial is obtained with the simple differentiation method. The numerator of higher order transfer function reduces by differentiation method and denominator reduces by many clustering techniques such as pole clustering, modified pole clustering, residue based pole clustering, fuzzy Cmeans clustering and stability equation method. The proposed method has been verified using with two numerical examples, first is used in the continuous time system and the second is used in the discrete time system.</abstract>
<date>May - July 2016</date>
<copyright>Copyright &copy; 2016 i-manager publications. All rights reserved.</copyright>
<publisher>i-manager Publications</publisher>
<article_url>http://www.imanagerpublications.com/Article.aspx?ArticleId=7065</article_url>