Classification of ancient Roman glazed ceramics using the neural network of Self-Organizing Maps

Artificial neural networks with unsupervised learning strategy known as Sel f-Organizing Maps were applied to classify ancient Roman glazed ceramics. T heir clay ceramic bodies were analyzed by Inductively Coupled Plasma-Atomic Emission Spectroscopy and the chemical composition obtained was processed by this neural algorithm. The results obtained provide two types of informa tion: firstly, classification of ceramic samples with identification of sev eral groups and secondly, differentiation between the elemental chemical in formation. It was found that there are certain chemical elements which can be considered as principal and which can serve to differentiate between cer amics, whereas other elements give redundant information and do not contrib ute to sample differentiation. Seven chemical elements were considered prin cipal and provide the necessary information. Two types of element were iden tified: 1 - a group formed by common elements, such as: Ca, Fe, Mg, Mn and 2 - another formed by optional elements: K or Na and Ba or Sr and Al or Ti.