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Volumes / Journal / 17 - 2006 / Deravignone L., Macchi Jánica G.

Artificial Neural Networks in archaeology

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Fig. 1 – ANN are based on a multilayer structure: input, hidden and output layers. Each layer is based on a set of units that are interconnected with a series of weighted links.

Fig. 1 – ANN are based on a multilayer structure: input, hidden and output layer...

Fig. 2 – The analysis process is based on a 5 steps process. For most of them, specific applications where developed.

Fig. 2 – The analysis process is based on a 5 steps process. For most of them, s...

Fig. 3 – The analysis area: south-eastern Tuscany including the Provinces of Grosseto, Siena and Arezzo. This specific analysis was achieved with 14 input variables: cost distance from watersheds and from towns, slope, elevation (DTM) and the relatives 300 meters shifts (North, North-East, East...), their mean and standard deviation.

Fig. 3 – The analysis area: south-eastern Tuscany including the Provinces of Gro...

Fig. 4 – Differences between analyses performed without and with surroundings values. Areas with high values are smaller in thesecond screenshot, highlighting theincreased precision of the analysis.

Fig. 4 – Differences between analyses performed without and with surroundings va...

Fig. 5 – Comparison of analyses results using a line chart. Each linerepresents thesequence of ordered values foreach castle taken from three different output raster surfaces (examples 1,2and3).Bysome aspects this chart may be used as a significance measurement for different networks.

Fig. 5 – Comparison of analyses results using a line chart. Each linerepresents ...

Fig. 6 – Detail of the analysis. Darker areas show a higher probability of castles presence even if the ANN is trained only on castles prior to 1150 A.D.

Fig. 6 – Detail of the analysis. Darker areas show a higher probability of castl...

Deravignone L., Macchi Jánica G. 2006, Artificial Neural Networks in archaeology, Archeologia e Calcolatori 17