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Model Reduction of Continuous and Discrete Time Systems using Differentiation Method with Many Clustering Techniques

Maneesh Kumar Gupta*, Awadhesh Kumar**

* PG Scholar, Department of Electrical Engineering, Madan Mohan Malviya University of Technology, Gorakhpur, UttarPradesh, India.

** Assistant Professor, Department of Electrical Engineering, Madan Mohan Malviya University of Technology, Gorakhpur, UttarPradesh, India.

Periodicity:May - July'2016
DOI : https://doi.org/10.26634/jic.4.3.7065

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.

Keywords

Model Order Reduction, Differentiation Method, Pole Clustering, Modified Pole Clustering, Dominant Pole Clustering, Fuzzy C-means Clustering, Stability Equation, Integral Square Error.

How to Cite this Article?

Gupta, M.K., and Kumar, A. (2016). Model Reduction of Continuous and Discrete Time Systems using Differentiation Method with Many Clustering Techniques. i-manager’s Journal on Instrumentation and Control Engineering, 4(3), 27-33. https://doi.org/10.26634/jic.4.3.7065

References

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[14]. Maneesh Kumar Gupta, and Awadhesh Kumar (2016). “Model Order Reduction using Chebyshev Polynomial, Stability Equation Method and Fuzzy C-means Clustering”. i-manager's Journal on Instrumentation and Control Engineering, Vol. 4(21), Feb-Apr 2016, Print ISSN 2321-113X, E-ISSN 2321-1148, pp. 7-13

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Model Reduction of Continuous and Discrete Time Systems using Differentiation Method with Many Clustering Techniques

Abstract

Keywords

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References

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