@inproceedings{5241dece5ff84ba291baed39dfe75dc7,
title = "APPLICATION OF NEURAL NETWORKS TO THE CONTROL OF BURGERS EQUATION WITH RANDOM FORCING",
abstract = "In this paper, the feasibility of using artificial neural network to obtain the control inputs for the flow governed by Burgers equation with random forcing is studied. The objective is to provide a real time active controller for the flow. The data obtained from the optimal (LQG) and robust (H∞) control are used to train the network. Due to the structure of a neural network, it is possible for the trained neural network to imitate the controller in real time. The network consists of a layer with 4 input neurons, 2 hidden layers, a layer with 1 output neuron, and a bias neuron. The network is trained using backpropagation method. Several data sets are used for training and testing the network. The trained network is tested for the generality and for fault tolerance. A simulation with the neural network controller is conducted for the flow modeled by the Burgers equation with random forcing input. The results show that the employment of neural network as a controller can be effective for flow control problems.",
author = "Yongseung Cho and Agarwal, {Ramesh K.} and Yogesh Tupe",
note = "Publisher Copyright: {\textcopyright} 1996 American Society of Mechanical Engineers (ASME). All rights reserved.; ASME 1996 International Mechanical Engineering Congress and Exposition, IMECE 1996 ; Conference date: 17-11-1996 Through 22-11-1996",
year = "1996",
doi = "10.1115/IMECE1996-0957",
language = "English",
series = "ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE)",
publisher = "American Society of Mechanical Engineers (ASME)",
pages = "81--85",
editor = "A.S. Lavine and U. Chandra and M.M. Chen and C.T. Crowe and U. Fritsching and {et al}, al",
booktitle = "Fluids Engineering",
}