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Wiklendt, Lukasz, 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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Official URL: http://www.springerlink.com/content/e7k584qw2h047v...
Abstract
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: | 10.1007/s00521-008-0187-1 |
| Depositing User: | Hamilton Editor |
| Date Deposited: | 18 Nov 2009 10:02 |
| Journal or Publication Title: | Neural Computing & Applications |
| Publisher: | Springer-Verlag |
| Refereed: | Yes |
| Related URLs: | |
| URI: | https://mu.eprints-hosting.org/id/eprint/1676 |
| 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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