NDLI: Unsupervised design of Artificial Neural Networks via multi-dimensional Particle Swarm Optimization

Content Provider	IEEE Xplore Digital Library
Author	Kiranyaz, S. Ince, T. Yildirim, A. Gabbouj, M.
Copyright Year	2008
Description	Author affiliation: Tampere Univ. of Technol., Tampere, Finland (Kiranyaz, S.; Gabbouj, M.) \|\| TUBITAK, Ankara, Turkey (Yildirim, A.) \|\| Izmir Univ. of Econ., Izmir, Turkey (Ince, T.)
Abstract	In this paper, we present a novel and efficient approach for automatic design of artificial neural networks (ANNs) by evolving to the optimal network configuration(s) within an architecture space. The evolution technique, the so-called multidimensional particle swarm optimization (MD PSO) re-forms the native structure of PSO particles in such a way that they can make inter-dimensional passes with a dedicated dimensional PSO process. So in a multidimensional search space where the optimum dimension is unknown, swarm particles can seek for both positional and dimensional optima. This eventually removes the necessity of setting a fixed dimension a priori, which is a common drawback for the family of swarm optimizers. With the proper encoding of the network configurations and parameters into particles, MD PSO can then seek for positional optimum in the error space and dimensional optimum in the architecture space. The optimum dimension converged at the end of a MD PSO process corresponds to a unique ANN configuration where the network parameters (connections, weights and biases) can then be resolved from the positional optimum reached on that dimension. The efficiency and performance of the proposed technique is demonstrated over one of the hardest synthetic problems. The experimental results show that MD PSO evolves to optimum or near-optimum networks in general.
Starting Page	1
Ending Page	4
File Size	256361
Page Count	4
File Format	PDF
ISBN	9781424421749
ISSN	10514651
DOI	10.1109/ICPR.2008.4761094
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-08
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Artificial neural networks Particle swarm optimization Feedforward systems Space technology Multidimensional systems Encoding Evolutionary computation Genetic algorithms Genetic programming Genetic mutations
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition

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