The Use of Artificial Neural Networks in Projectile Point Typology

Abstract

Regional empirical typologies have received criticisms for characterizing artifact types as immutable, mutually exclusive groups, rather than analytical groups developed for an analytical purposes. The research presented here uses an empirical regional typology to supply data for the creation of analytical units. Developed in the field of computer science as a means to discover complex patterns in data sets, archaeologists can use artificial neural networks to identify analytical patterns in typological data. In this paper we explain what neural networks are and how they work. We show how a specific architecture and training process can be applied to artificial neural networks to create analytical units from empirical units. Finally, we provide four examples to show that the networks will not find a pattern where none exists, and to demonstrate how artificial neural networks can be applied to solve more complex problems.