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Investigating the Effectiveness of DMD and its Variants for Complex Data Analysis

Publication Type : Conference Paper

Thematic Areas : Center for Computational Engineering and Networking (CEN)

Publisher : IEEE

Source : 2019 International Conference on Intelligent Computing and Control Systems (ICCS), IEEE, Madurai, India (2019)

Url : https://ieeexplore.ieee.org/abstract/document/9065685

Campus : Coimbatore

School : School of Engineering

Center : Center for Computational Engineering and Networking, Computational Engineering and Networking

Department : Electronics and Communication

Year : 2019

Abstract : The unprecedented availability of data in various fields reinforces the need for more comprehensive and advanced data-driven algorithms to deal with it. The data-driven techniques are able to handle the large volume of heterogeneous data and to extract more valuable information regarding the underlying system. This paper investigates the effectiveness of modern data-driven methods for data from non-linear systems. The effectiveness of dynamic mode decomposition (DMD) and its variants such as rSVD-DMD, randomized DMD (RDMD) and total DMD (TDMD) for dta from nonlinear system is investigated. The data considered for this study are cylindrical fluid flow and neuro recordings.

Cite this Research Publication : Akshay, S., Gopalakrishnan, E. A. and Soman, K. P. “Investigating the Effectiveness of DMD and its variants for Complex Data Analysis”. International Conference on Intelligent Computing and Control Systems. May 15 – 17, 2019, Madurai, India.

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