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Augmentation of Elman Recurrent Network learning with particle swarm optimization

Ab Aziz, Mohamad Firdaus and Abdull Hamed, Haza Nuzly and Shamsuddin, Siti Mariyam (2008) Augmentation of Elman Recurrent Network learning with particle swarm optimization. In: Proceedings - 2nd Asia International Conference on Modelling and Simulation, AMS 2008. IEEE, New York, 625-630 . ISBN 978-076953136-6

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Official URL: http://dx.doi.org/10.1109/AMS.2008.50

Abstract

Despite a variety of Artificial Neural Network (ANN) categories, Backpropagation Network (BP) and Elman Recurrent Network (ERN) are the widespread modus operandi in real applications. However, there are many drawbacks in BP network, for instance, confinement in finding local minimum and may get stuck at regions of a search space or trap in local minima. To solve these problems, various optimization techniques such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) have been executed to improve ANN performance. In this study, we exploit errors optimization of Elman Recurrent Network with Backpropagation (ERNBP) and Elman Recurrent Network with Particle Swarm Optimization (ERNPSO) to probe the performance of both networks. The comparisons are done with PSO that is integrated with Neural Network (PSONN) and GA with Neural Network (GANN). The results show that ERNPSO furnishes promising outcomes in terms of classification accuracy and convergence rate compared to ERNBP, PSONN and GANN

Item Type:Book Section
Additional Information:ISBN: 978-076953136-6; 2nd Asia International Conference on Modelling and Simulation, AMS 2008; Kuala Lumpur; 13 May 2008 through 15 May 2008
Uncontrolled Keywords:artificial neural network, backpropagation network, elman recurrent network, particle swarm optimization, recurrent network
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Computer Science and Information System (Formerly known)
ID Code:12505
Deposited By: Liza Porijo
Deposited On:06 Jun 2011 08:43
Last Modified:06 Jun 2011 08:43

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