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    A small spiking neural network with LQR control applied to the acrobot

    Wiklendt, Lukasz and Chalup, Stephan and Middleton, Rick (2009) A small spiking neural network with LQR control applied to the acrobot. Neural Computing & Applications , 18 (4). pp. 369-375. ISSN 1433-3058

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    This paper presents the results of a computer simulation which, combined a small network of spiking neurons with linear quadratic regulator (LQR) control to solve the acrobot swing-up and balance task. To our knowledge, this task has not been previously solved with spiking neural networks. Input to the network was drawn from the state of the acrobot, and output was torque, either directly applied to the actuated joint, or via the switching of an LQR controller designed for balance. The neural network’s weights were tuned using a (µ λ)-evolution strategy without recombination, and neurons’ parameters, were chosen to roughly approximate biological neurons.

    Item Type: Article
    Keywords: Spiking neural networks; Acrobot; LQR; Evolution; Hamilton Institute.
    Academic Unit: Faculty of Science and Engineering > Electronic Engineering
    Faculty of Science and Engineering > Mathematics and Statistics
    Item ID: 1676
    Identification Number:
    Depositing User: Hamilton Editor
    Date Deposited: 18 Nov 2009 10:02
    Journal or Publication Title: Neural Computing & Applications
    Publisher: Springer-Verlag
    Refereed: Yes
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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