Publication Type : Conference Paper
Publisher : 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI),
Source : 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Udupi, India (2017)
Url : https://ieeexplore.ieee.org/document/8126104
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 : R. Mohan, Dr. E. A. Gopalakrishnan, and K. P. Soman, “Classification of states of bi-stable oscillator using deep learning”, in 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Udupi, India, 2017.