An optimal full-genetic technique used to train RBF neural networks
| Autor: | Petrica Ciotirnae, Iulian-Constantin Vizitiu, Adrian Stoica, Radu Adrian, Cristian Molder, Ioan Nicolaescu |
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| Rok vydání: | 2010 |
| Předmět: |
Artificial neural network
business.industry Computer science Pattern recognition Statistical classification ComputingMethodologies_PATTERNRECOGNITION Pattern recognition (psychology) Genetic algorithm Algorithm design Artificial intelligence Cluster analysis business Global optimization Hierarchical RBF |
| Zdroj: | 2010 9th International Symposium on Electronics and Telecommunications. |
| DOI: | 10.1109/isetc.2010.5679259 |
| Popis: | It is well-known that, the pattern recognition performances assigned to RBF neural networks depends a lot by their specific training algorithms, and by the methods used for RBF center selection (e.g., a clustering technique), particularly. Having as starting point the membership of genetic algorithms to the powerful class of global optimization methods, an optimal full-genetic training procedure of RBF neural networks based on hybrid genetic clustering algorithm used for center mapping, and on genetic approach to fit the output neural weights is proposed. Finally, using a real pattern recognition task, a comparative study (as performance level) with others standard RBF training methods and SART neural network is also described. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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