Simulated annealing technique for fast learning of SOM networks

Salvatore Gaglio, Antonino Fiannaca, Alfonso Urso, Giuseppe Di Fatta, Salvatore Gaglio, Riccardo Rizzo

Risultato della ricerca: Articlepeer review

11 Citazioni (Scopus)


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.
Lingua originaleEnglish
pagine (da-a)889-899
Numero di pagine11
Stato di pubblicazionePublished - 2013

All Science Journal Classification (ASJC) codes

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