Dynamic Cluster Recognition with Multiple Self-Organising Maps
Autori: Distante C., Siciliano P., Persaud K.C.
Affiliazione autori: Univ Lecce, Dipartimento Ingn Innovazione, Via Monteroni, I-73100 Lecce, Italy;
IME – CNR, Lecce, Italy;
DIAS – UMIST, Manchester, UK
Abstract: A neural architecture, based on several self-organising maps, is presented which counteracts the parameter drift problem for an array of conducting polymer gas sensors when used for odour sensing. The neural architecture is named mSom, where m is the number of odours to be recognised, and is mainly constituted of m maps; each one approximates the statistical distribution of a given odour. Competition occurs both within each map and between maps for the selection of the minimum map distance in the Euclidean space. The network (mSom) is able to adapt itself to new changes of the input probability distribution by repetitive self-training processes based on its experience. This architecture has been tested and compared with other neural architectures, such as RBF and Fuzzy ARTMAP. The network shows long-term stable behaviour, and is completely autonomous during the testing phase, where re-adaptation of the neurons is needed due to the changes of the input probability distribution of the given data set.
Volume: 5 (3) Da Pagina: 306 A: 315
Parole chiavi: Dynamic cluster recognition