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Publication Type : Conference Paper
Publisher : 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI),
Source : International Conference on Advances in Computing, Communications and Informatics, September 13-16, 2017, Manipal, India.
Campus : Coimbatore
School : School of Engineering
Center : Automotive Center, Computational Engineering and Networking
Department : Mechanical Engineering, Computer Science, Sciences
Year : 2017
Abstract : Study of critical transitions and early warning measures are of great importance for dealing with any complex system. Manually selected statistical features with handpicked parameters have been used in a wide variety of fields for this purpose. We envision the use of deep learning architectures like simple feed forward networks (FFN), convolutional neural networks (CNN) and long short-term memory networks (LSTM) to predict these critical transitions from raw time-series data obtained from complex systems with minimal human interference in parameter choosing. As a first step towards this goal, in this study we use the above mentioned deep learning architectures to classify the states of a modified Van der Pol oscillator. We observe that the deep learning architectures produce good classification results and show promise as a tool for detection of critical transitions from raw time-series data.
Cite this Research Publication : Rohit, M., Gopalakrishnan, E. A. and Soman, K. P. “Classification of states of bi-stable oscillator using deep learning.” International Conference on Advances in Computing, Communications and Informatics, September 13-16, 2017, Manipal, India.