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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Melin, P. Mancilla, A. Lopez, M. Soria, J. Castillo, O. |
| Copyright Year | 2007 |
| Description | Author affiliation: Tijuana Inst. of Technol., Tijuana (Melin, P.; Castillo, O.) |
| Abstract | We describe in this paper the evolution of modular neural networks using hierarchical genetic algorithms for pattern recognition. Modular neural networks (MNN) have shown significant learning improvement over single neural networks (NN). For this reason, the use of MNN for pattern recognition is well justified. However, network topology design of MNN is at least an order of magnitude more difficult than for classical NNs. We describe in this paper the use of a hierarchical genetic algorithm (HGA) for optimizing the topology of each of the neural network modules of the MNN. The HGA is clearly needed due to the fact that topology optimization requires that we are able to manage both the layer and node information for each of the MNN modules. Simulation results prove the feasibility and advantages of the proposed approach. |
| Starting Page | 2977 |
| Ending Page | 2981 |
| File Size | 296034 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781424413799 |
| ISSN | 10987576 |
| DOI | 10.1109/IJCNN.2007.4371434 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-08-12 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Pattern recognition Monitoring Fuzzy neural networks Neural networks Fingerprint recognition Authentication Multi-layer neural network Face recognition Iris recognition Speech recognition |
| Content Type | Text |
| Resource Type | Article |
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