NDLI: A Natural Algorithmic Approach to the Structural Optimisation of Neural Networks

Content Provider	IEEE Xplore Digital Library
Author	Suraweera, N.P. Ranasinghe, D.N.
Copyright Year	2008
Description	Author affiliation: Dept. of Phys., Univ. of Colombo, Colombo (Suraweera, N.P.) \|\| Sch. of Comput., Dept. of Comput. & Intell. Syst., Univ. of Colombo, Colombo (Ranasinghe, D.N.)
Abstract	Structural design of an artificial neural network 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 contribution to the performance of a neural network, though it is typically decided in an adhoc manner. In this paper, the structure of a neural network is adaptively optimised by determining the number of hidden layers and hidden nodes that give the optimal performance in a given problem domain. An optimisation approach was developed based on the particle swarm optimisation (PSO) algorithm, which is a simple, easy to implement but highly effective evolutionary algorithm which uses a cooperative approach. This approach adaptively optimises an artificial neural network built for a specific problem domain by evolving both the hidden layers and the number of nodes in a particular hidden layer. It has 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 artificial neural networks.
Starting Page	150
Ending Page	156
File Size	256722
Page Count	7
File Format	PDF
ISBN	9781424428991
DOI	10.1109/ICIAFS.2008.4783967
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-12
Publisher Place	Sri Lanka
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Humans Optimization methods Artificial neural networks hidden layer and hidden node adjustments Particle Swarm Optimisation Particle swarm optimization Biological neural networks Design optimization Physics computing Artificial Neural Networks Neural networks Computer networks
Content Type	Text
Resource Type	Article

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