Qasem, Sultan Noman and Shamsuddin, Siti Mariyam (2009) Improving generalization of radial basis function network with adaptive multi-objective particle swarm optimization. In: 2009 IEEE International Conference on Systems, Man and Cybernetics. Article number 5346876 . IEEE, pp. 534-540. ISBN 978-142442794-9
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Official URL: http://dx.doi.org/10.1109/ICSMC.2009.5346876
In this paper, an adaptive evolutionary multiobjective selection method of RBF Networks structure is discussed. The candidates of RBF Network structures are encoded into particles in Particle Swarm Optimization (PSO). These particles evolve toward Pareto-optimal front defined by several objective functions with model accuracy and complexity. The problem of unsupervised and supervised learning is discussed with Adaptive Multi-Objective PSO (AMOPSO). This study suggests an approach of RBF Network training through simultaneous optimization of architectures and weights with Adaptive PSO-based multi-objective algorithm. Our goal is to determine whether Adaptive Multi-objective PSO can train RBF Networks, and the performance is validated on accuracy and complexity. The experiments are conducted on two benchmark datasets obtained from the machine learning repository. The results show that our proposed method provides an effective means for training RBF Networks that is competitive with PSO-based multi-objective algorithm.
|Item Type:||Book Section|
|Additional Information:||2009 IEEE International Conference on Systems, Man and Cybernetics, SMC 2009; San Antonio, TX; 11 October 2009 through 14 October 2009; ISSN : 1062-922X|
|Uncontrolled Keywords:||adaptive multiobjective particle swarm optimization, multi-objective particle swarm optimization, radial basis function network|
|Subjects:||Q Science > QA Mathematics > QA75 Electronic computers. Computer science|
|Divisions:||Computer Science and Information System (Formerly known)|
|Deposited By:||Ms Zalinda Shuratman|
|Deposited On:||30 Sep 2011 15:06|
|Last Modified:||30 Sep 2011 15:06|
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