Hybrid PSO-black stork foraging for functional neural fuzzy network learning enhancement
Fuzzy Neural Networks consider one of the most important computational tools which are applied in many areas such as classification, pattern recognition and medical diagnosis. The learning process is very crucial for fuzzy neural network to be powerful in solving problems. In this study, a hybrid bl...
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| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Published: |
2012
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/47062/ http://dx.doi.org/10.1109/ICSMC.2012.6377919 |
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| Institution: | Universiti Teknologi Malaysia |
| Summary: | Fuzzy Neural Networks consider one of the most important computational tools which are applied in many areas such as classification, pattern recognition and medical diagnosis. The learning process is very crucial for fuzzy neural network to be powerful in solving problems. In this study, a hybrid black stork foraging process based on particle swarm optimization (BSFP-PSO) is used to enhance the learning of new existing approach of fuzzy neural network called functional neural fuzzy network (FNFN). Classification problem have been adopted to assess the performance of the new proposed model black stork foraging process hybrid particle swarm optimization and functional neural fuzzy network. In conclusion, the experimental results have shown that the performance of the proposed model is better than the performance of standard particle swarm optimization with functional neural fuzzy network for solving Iris and Breast cancer classification in terms of error rate and classification accuracy. |
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