Universiti Teknologi Malaysia Institutional Repository

Hybrid PSO-black stork foraging for functional neural fuzzy network learning enhancement

Hamed, Z. A. and Hashim, S. Z. M. (2012) Hybrid PSO-black stork foraging for functional neural fuzzy network learning enhancement. In: 2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012, 14-17 Oct, 2012, Seoul, South Korea.

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Official URL: http://dx.doi.org/10.1109/ICSMC.2012.6377919

Abstract

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.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:Functional neural fuzzy network, Fuzzy neural network, Particle swarm optimization
Subjects:Q Science
Divisions:Computer Science and Information System (Formerly known)
ID Code:47062
Deposited By: Fazli Masari
Deposited On:22 Jun 2015 13:56
Last Modified:14 Oct 2018 16:21

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