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On the Use of Dropouts in Neural Networks for System Identification and Control

Yadav, SM and George, K (2019) On the Use of Dropouts in Neural Networks for System Identification and Control. In: UNSPECIFIED, pp. 1374-1381.

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Official URL: https://dx.doi.org/10.1109/SSCI.2018.8628706

Abstract

As universal function approximators, neural networks have been successfully used for nonlinear dynamical system identification and control. Recent developments in neural network regularisation methods include dropouts. Here, the outputs of hidden units are dropped randomly with a probability which is tuned as a hyperparameter. In this paper, we first present a scheme to conduct a rigorous analysis on the effect of changing hyperparameters in neural network learning by comparing their ability to generalise and achieve convergence. Second, we conduct an analysis with dropouts using the proposed scheme through a grid search over the dropout probabilities in each hidden layer. In the context of system identification and control, our results show that dropout is at best as good as standard back propagation in terms of the amount of data required for generalisation without adapting. This is achieved by the proposed scheme which efficiently summarises hundreds of simulations with typical examples. © 2018 IEEE.

Item Type: Conference Poster
Additional Information: Copyright for this article belongs to Institute of Electrical and Electronics Engineers Inc.
Keywords: Artificial intelligence; Backpropagation; Dynamical systems; Identification (control systems); Neural networks; Nonlinear dynamical systems; Nonlinear systems; Religious buildings, Adaptive Control; dropouts; Generalisation; Hyper-parameter; Hyperparameters; Neural network learning; Rigorous analysis; Universal functions, Adaptive control systems
Department/Centre: Division of Interdisciplinary Research > Robert Bosch Centre for Cyber Physical Systems
Depositing User: Id for Latest eprints
Date Deposited: 15 Apr 2019 05:20
Last Modified: 15 Apr 2019 05:20
URI: http://eprints.iisc.ac.in/id/eprint/62095

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