MIMO Lyapunov theory-based RBF neural classifier for traffic sign recognition
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 iterati...
| Main Authors: | , , |
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| Format: | Journal Article |
| Published: |
Hindawi
2012
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| Online Access: | http://hdl.handle.net/20.500.11937/48247 |
| _version_ | 1848758056883585024 |
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| author | Lim, King Hann Seng, Kah Phooi Ang, Li-Minn |
| author_facet | Lim, King Hann Seng, Kah Phooi Ang, Li-Minn |
| author_sort | Lim, King Hann |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T09:37:56Z |
| format | Journal Article |
| id | curtin-20.500.11937-48247 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:37:56Z |
| publishDate | 2012 |
| publisher | Hindawi |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-482472017-09-13T15:57:41Z MIMO Lyapunov theory-based RBF neural classifier for traffic sign recognition Lim, King Hann Seng, Kah Phooi Ang, Li-Minn 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. 2012 Journal Article http://hdl.handle.net/20.500.11937/48247 10.1155/2012/793176 Hindawi unknown |
| spellingShingle | Lim, King Hann Seng, Kah Phooi Ang, Li-Minn MIMO Lyapunov theory-based RBF neural classifier for traffic sign recognition |
| title | MIMO Lyapunov theory-based RBF neural classifier for traffic sign recognition |
| title_full | MIMO Lyapunov theory-based RBF neural classifier for traffic sign recognition |
| title_fullStr | MIMO Lyapunov theory-based RBF neural classifier for traffic sign recognition |
| title_full_unstemmed | MIMO Lyapunov theory-based RBF neural classifier for traffic sign recognition |
| title_short | MIMO Lyapunov theory-based RBF neural classifier for traffic sign recognition |
| title_sort | mimo lyapunov theory-based rbf neural classifier for traffic sign recognition |
| url | http://hdl.handle.net/20.500.11937/48247 |