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Published VersionCC BY V3.0, Open Access
Journal article
MIMO Lyapunov Theory-Based RBF Neural Classifier for Traffic Sign Recognition
Applied Computational Intelligence and Soft Computing, Vol.2012, pp.1-7
2012
Abstract
Lyapunov theory-based radial basis function neural network (RBFNN) is developed for traffic sign recognition in this paper to perform multiple inputs multiple outputs (MIMO) classification. Multidimensional input is inserted into RBF nodes and these nodes are linked with multiple weights. An iterative weight adaptation scheme is hence designed with regards to the Lyapunov stability theory to obtain a set of optimum weights. In the design, the Lyapunov function has to be well selected to construct an energy space with a single global minimum. Weight gain is formed later to obey the Lyapunov stability theory. Detail analysis and discussion on the proposed classifier’s properties are included in the paper. The performance comparisons between the proposed classifier and some existing conventional techniques are evaluated using traffic sign patterns. Simulation results reveal that our proposed system achieved better performance with lower number of training iterations.
Details
- Title
- MIMO Lyapunov Theory-Based RBF Neural Classifier for Traffic Sign Recognition
- Authors
- King Hann Lim (Author) - Curtin University SarawakKah Phooi Seng (Author) - Sunway UniversityLi-Minn Ang (Author) - Edith Cowan University
- Publication details
- Applied Computational Intelligence and Soft Computing, Vol.2012, pp.1-7
- Publisher
- Hindawi Limited
- Date published
- 2012
- DOI
- 10.1155/2012/793176
- ISSN
- 1687-9732; 1687-9724
- Copyright note
- Copyright © 2012 King Hann Lim et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- Organisation Unit
- University of the Sunshine Coast, Queensland; School of Science, Technology and Engineering; Engage Research Lab
- Language
- English
- Record Identifier
- 99569199702621
- Output Type
- Journal article
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