Optimization and stabilization of sequential learning in RBF network for nonlinear function approximation
This paper proposes a solution for inconsistency pruning of neurons within a sequential learning Radial Basis Function (RBF) Network. This paper adopts the concept that a specific RBF neuron which continuously exhibits low output in a sequence of training patterns does not justify the proposition th...
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| Format: | Article |
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IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG
2008
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| Online Access: | http://shdl.mmu.edu.my/1947/ |
| _version_ | 1848789922155069440 |
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| author | Lim, W. S. Yeoh, J. W. L. |
| author_facet | Lim, W. S. Yeoh, J. W. L. |
| author_sort | Lim, W. S. |
| building | MMU Institutional Repository |
| collection | Online Access |
| description | This paper proposes a solution for inconsistency pruning of neurons within a sequential learning Radial Basis Function (RBF) Network. This paper adopts the concept that a specific RBF neuron which continuously exhibits low output in a sequence of training patterns does not justify the proposition that the neuron is insignificant to the whole function to be approximated. We establish additional criterions to provide protection from error in pruning RBF neurons within the hidden layer, which we prove is able to improve consistency and stability of neuron evolution. With such stability within the sequential learning process, we also show how the convergence speed of the network can be improved by reducing the number of consecutive observations required to prune a neuron in the hidden layer. |
| first_indexed | 2025-11-14T18:04:25Z |
| format | Article |
| id | mmu-1947 |
| institution | Multimedia University |
| institution_category | Local University |
| last_indexed | 2025-11-14T18:04:25Z |
| publishDate | 2008 |
| publisher | IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | mmu-19472011-08-08T07:40:09Z http://shdl.mmu.edu.my/1947/ Optimization and stabilization of sequential learning in RBF network for nonlinear function approximation Lim, W. S. Yeoh, J. W. L. T Technology (General) TA Engineering (General). Civil engineering (General) This paper proposes a solution for inconsistency pruning of neurons within a sequential learning Radial Basis Function (RBF) Network. This paper adopts the concept that a specific RBF neuron which continuously exhibits low output in a sequence of training patterns does not justify the proposition that the neuron is insignificant to the whole function to be approximated. We establish additional criterions to provide protection from error in pruning RBF neurons within the hidden layer, which we prove is able to improve consistency and stability of neuron evolution. With such stability within the sequential learning process, we also show how the convergence speed of the network can be improved by reducing the number of consecutive observations required to prune a neuron in the hidden layer. IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG 2008-12 Article NonPeerReviewed Lim, W. S. and Yeoh, J. W. L. (2008) Optimization and stabilization of sequential learning in RBF network for nonlinear function approximation. IEICE Electronics Express, 5 (23). pp. 1030-1035. ISSN 1349-2543 http://dx.doi.org/10.1587/elex.5.1030 doi:10.1587/elex.5.1030 doi:10.1587/elex.5.1030 |
| spellingShingle | T Technology (General) TA Engineering (General). Civil engineering (General) Lim, W. S. Yeoh, J. W. L. Optimization and stabilization of sequential learning in RBF network for nonlinear function approximation |
| title | Optimization and stabilization of sequential learning in RBF network for nonlinear function approximation |
| title_full | Optimization and stabilization of sequential learning in RBF network for nonlinear function approximation |
| title_fullStr | Optimization and stabilization of sequential learning in RBF network for nonlinear function approximation |
| title_full_unstemmed | Optimization and stabilization of sequential learning in RBF network for nonlinear function approximation |
| title_short | Optimization and stabilization of sequential learning in RBF network for nonlinear function approximation |
| title_sort | optimization and stabilization of sequential learning in rbf network for nonlinear function approximation |
| topic | T Technology (General) TA Engineering (General). Civil engineering (General) |
| url | http://shdl.mmu.edu.my/1947/ http://shdl.mmu.edu.my/1947/ http://shdl.mmu.edu.my/1947/ |