Universiti Teknologi Malaysia Institutional Repository

Radial basis function network based on multi-objective particle swarm optimization

Qasem, Sultan Noman and Shamsuddin, Siti Mariyam (2009) Radial basis function network based on multi-objective particle swarm optimization. In: 2009 6th International Symposium on Mechatronics and its Applications, ISMA 2009. Institute of Electrical and Electronics Engineers, New York, pp. 481-486. ISBN 978-142443481-7

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Official URL: http://dx.doi.org/10.1109/ISMA.2009.5164833

Abstract

The problem of unsupervised and supervised learning is discussed within the context of multi-objective optimization. In this paper, an evolutionary multi-objective selection method of RBF Networks structure is discussed. The candidates of RBF Network structure are encoded into the particles in PSO. Then, they evolve toward Pareto-optimal front defined by several objective functions concerning with model accuracy and model complexity. This study suggests an approach of RBF Network training through simultaneous optimization of architectures and weights with PSO-based multi-objective algorithm. Our goal is to determine whether Multi-objective PSO can train RBF Networks, and the performance is validated on accuracy and complexity. The experiments are conducted on benchmark datasets obtained from the UCI machine learning repository. The results show that our proposed method provides an effective means for training RBF Networks that is competitive with other evolutionary computational-based methods.

Item Type:Book Section
Additional Information:2009 6th International Symposium on Mechatronics and its Applications, ISMA 2009; Sharjah; 23 March 2009 through 26 March 2009
Uncontrolled Keywords:benchmark datasets, model accuracy, model complexity, multi objective, multi objective algorithm, multi objective particle swarm optimization, objective functions, pareto-optimal front, radial basis functions, RBF network, selection methods, simultaneous optimization, UCI machine learning repository
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Computer Science and Information System (Formerly known)
ID Code:13080
Deposited By: Liza Porijo
Deposited On:18 Jul 2011 01:42
Last Modified:18 Jul 2011 01:42

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