Please use this identifier to cite or link to this item: https://doi.org/10.1109/TNN.2006.881710
Title: Neural networks for continuous online learning and control
Authors: Choy, M.C.
Srinivasan, D. 
Cheu, R.L. 
Keywords: Distributed control
Hybrid model
Neural control
Online learning
Traffic signal control
Issue Date: Nov-2006
Source: Choy, M.C., Srinivasan, D., Cheu, R.L. (2006-11). Neural networks for continuous online learning and control. IEEE Transactions on Neural Networks 17 (6) : 1511-1531. ScholarBank@NUS Repository. https://doi.org/10.1109/TNN.2006.881710
Abstract: This paper proposes a new hybrid neural network (NN) model that employs a multistage online learning process to solve the distributed control problem with an infinite horizon. Various techniques such as reinforcement learning and evolutionary algorithm are used to design the multistage online learning process. For this paper, the infinite horizon distributed control problem is implemented in the form of real-time distributed traffic signal control for intersections in a large-scale traffic network. The hybrid neural network model is used to design each of the local traffic signal controllers at the respective intersections. As the state of the traffic network changes due to random fluctuation of traffic volumes, the NN-based local controllers will need to adapt to the changing dynamics in order to provide effective traffic signal control and to prevent the traffic network from becoming overcongested. Such a problem is especially challenging if the local controllers are used for an infinite horizon problem where online learning has to take place continuously once the controllers are implemented into the traffic network. A comprehensive simulation model of a section of the Central Business District (CBD) of Singapore has been developed using PARAMICS microscopic simulation program. As the complexity of the simulation increases, results show that the hybrid NN model provides significant improvement in traffic conditions when evaluated against an existing traffic signal control algorithm as well as a new, continuously updated simultaneous perturbation stochastic approximation-based neural network (SPSA-NN). Using the hybrid NN model, the total mean delay of each vehicle has been reduced by 78% and the total mean stoppage time of each vehicle has been reduced by 84% compared to the existing traffic signal control algorithm. This shows the efficacy of the hybrid NN model in solving large-scale traffic signal control problem in a distributed manner. Also, it indicates the possibility of using the hybrid NN model for other applications that are similar in nature as the infinite horizon distributed control problem. © 2006 IEEE.
Source Title: IEEE Transactions on Neural Networks
URI: http://scholarbank.nus.edu.sg/handle/10635/50717
ISSN: 10459227
DOI: 10.1109/TNN.2006.881710
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