Lyapunov theory-based multilayered neural network
This brief presents a Lyapunov theory-based weight adaptation scheme for a multilayered neural network (MLNN) mainly used to classify a multiple-input-multiple-output (MIMO) problem. Initially, the MLNN system is linearized using Taylor series expansion. Then, the weight adaptation scheme is designe...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
IEEE Circuits and Systems Society
2009
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| Online Access: | http://hdl.handle.net/20.500.11937/26780 |
| _version_ | 1848752083608535040 |
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| author | Lim, Hann Seng, K. Ang, L. Chin, S. |
| author_facet | Lim, Hann Seng, K. Ang, L. Chin, S. |
| author_sort | Lim, Hann |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This brief presents a Lyapunov theory-based weight adaptation scheme for a multilayered neural network (MLNN) mainly used to classify a multiple-input-multiple-output (MIMO) problem. Initially, the MLNN system is linearized using Taylor series expansion. Then, the weight adaptation scheme is designed based on the Lyapunov stability theory to iteratively update the weight. In the design, the Lyapunov function has to be well selected to construct an energy space with a single global minimum. Hence, the Lyapunov theory-based MLNN acts as a MIMO classifier for face recognition. Analysis and discussion on Lyapunov properties of the proposed classifier are included. The performance of the proposed technique is tested on the Olivetti Research Laboratory database for face classification, and some comparisons with existing conventional techniques are given. Simulation results have revealed that our proposed system achieved better performance. © 2009 IEEE. |
| first_indexed | 2025-11-14T08:02:59Z |
| format | Journal Article |
| id | curtin-20.500.11937-26780 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:02:59Z |
| publishDate | 2009 |
| publisher | IEEE Circuits and Systems Society |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-267802017-09-13T15:29:35Z Lyapunov theory-based multilayered neural network Lim, Hann Seng, K. Ang, L. Chin, S. This brief presents a Lyapunov theory-based weight adaptation scheme for a multilayered neural network (MLNN) mainly used to classify a multiple-input-multiple-output (MIMO) problem. Initially, the MLNN system is linearized using Taylor series expansion. Then, the weight adaptation scheme is designed based on the Lyapunov stability theory to iteratively update the weight. In the design, the Lyapunov function has to be well selected to construct an energy space with a single global minimum. Hence, the Lyapunov theory-based MLNN acts as a MIMO classifier for face recognition. Analysis and discussion on Lyapunov properties of the proposed classifier are included. The performance of the proposed technique is tested on the Olivetti Research Laboratory database for face classification, and some comparisons with existing conventional techniques are given. Simulation results have revealed that our proposed system achieved better performance. © 2009 IEEE. 2009 Journal Article http://hdl.handle.net/20.500.11937/26780 10.1109/TCSII.2009.2015400 IEEE Circuits and Systems Society restricted |
| spellingShingle | Lim, Hann Seng, K. Ang, L. Chin, S. Lyapunov theory-based multilayered neural network |
| title | Lyapunov theory-based multilayered neural network |
| title_full | Lyapunov theory-based multilayered neural network |
| title_fullStr | Lyapunov theory-based multilayered neural network |
| title_full_unstemmed | Lyapunov theory-based multilayered neural network |
| title_short | Lyapunov theory-based multilayered neural network |
| title_sort | lyapunov theory-based multilayered neural network |
| url | http://hdl.handle.net/20.500.11937/26780 |