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Adaptive Structural Optimisation of Neural Networks

Authors:

NP Suraweera ,

Department of Physics, University of Colombo, Sri Lanka, LK
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DN Ranasinghe

University of Colombo School of Computing, Sri Lanka, LK
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Abstract

Structural design of an artificial neural network (ANN) is a very important phase in the construction of such a network. The selection of the optimal number of hidden layers and hidden nodes has a significant impact on the performance of a neural network, though typically decided in an adhoc manner. In this paper, the structure of a neural network is adaptively optimised by determine the number of hidden layers and hidden nodes that give the optimal performance in a given problem domain. Two optimisation approaches have been developed based on the Particle Swarm Optimisation (PSO) algorithm, which is an evolutionary algorithm which uses a cooperative approach. These approaches have been applied on two well known case studies in the classification domain, namely the Iris data classification and the Ionosphere data classification.
The obtained results and comparisons done with past research work has clearly shown that this method of optimisation is by far, the best approach for adaptive structural optimisation of ANNs.

Keywords: neural networks, particle swarm optimization, weight adjestment, hidden layer adjustment.

doi: 10.4038/icter.v1i1.450

The International Journal on Advances in ICT for Emerging Regions 2008 01 (01) : 33 - 41

How to Cite: Suraweera, N. & Ranasinghe, D., (2009). Adaptive Structural Optimisation of Neural Networks. International Journal on Advances in ICT for Emerging Regions (ICTer). 1(1), pp.33–41. DOI: http://doi.org/10.4038/icter.v1i1.450
Published on 26 Mar 2009.
Peer Reviewed

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