![](/images/graphics-bg.png)
MIMO Lyapunov Theory-Based RBF Neural Classifier for Traffic Sign Recognition
Joint Authors
Ang, Li-Minn
Seng, Kah Phooi
Lim, King Hann
Source
Applied Computational Intelligence and Soft Computing
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-05-09
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Lim, King Hann& Seng, Kah Phooi& Ang, Li-Minn. 2012. MIMO Lyapunov Theory-Based RBF Neural Classifier for Traffic Sign Recognition. Applied Computational Intelligence and Soft Computing،Vol. 2012, no. 2012, pp.1-7.
https://search.emarefa.net/detail/BIM-498553
Modern Language Association (MLA)
Lim, King Hann…[et al.]. MIMO Lyapunov Theory-Based RBF Neural Classifier for Traffic Sign Recognition. Applied Computational Intelligence and Soft Computing No. 2012 (2012), pp.1-7.
https://search.emarefa.net/detail/BIM-498553
American Medical Association (AMA)
Lim, King Hann& Seng, Kah Phooi& Ang, Li-Minn. MIMO Lyapunov Theory-Based RBF Neural Classifier for Traffic Sign Recognition. Applied Computational Intelligence and Soft Computing. 2012. Vol. 2012, no. 2012, pp.1-7.
https://search.emarefa.net/detail/BIM-498553
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-498553