Accessibility navigation

Simulated annealing technique for fast learning of SOM networks

Fiannaca, A., Di Fatta, G., Rizzo, R., Urso, A. and Gaglio, S. (2013) Simulated annealing technique for fast learning of SOM networks. Neural Computing and Applications, 22 (5). 889-899 . ISSN 0941-0643

Full text not archived in this repository.

It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

To link to this item DOI: 10.1007/s00521-011-0780-6


The Self-Organizing Map (SOM) is a popular unsupervised neural network able to provide effective clustering and data visualization for multidimensional input datasets. In this paper, we present an application of the simulated annealing procedure to the SOM learning algorithm with the aim to obtain a fast learning and better performances in terms of quantization error. The proposed learning algorithm is called Fast Learning Self-Organized Map, and it does not affect the easiness of the basic learning algorithm of the standard SOM. The proposed learning algorithm also improves the quality of resulting maps by providing better clustering quality and topology preservation of input multi-dimensional data. Several experiments are used to compare the proposed approach with the original algorithm and some of its modification and speed-up techniques.

Item Type:Article
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:26262
Uncontrolled Keywords:SOM, simulated annealing, clustering, fast learning

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation