ABSTRACT Artificial neural networks represent an alternative to traditional multivariate techniques, such as principal component and discriminant analysis, which rely on hypotheses regarding the normal distribution of the data and homoscedasticity. They also may be a powerful tool for multivariate modeling of systems that do not present linear correlation between variables, as well as to visualize high-dimensional data in bi- or trivariate structures. One special kind of neural network of interest in archaeometric studies is the Self-Organizing Map (SOM). SOMs can be distinguished from other neural networks for preserving the topological features of the original multivariate space. In this study, the self-organizing maps were applied to concentration data of chemical elements measured in archaeological ceramics from Central Amazon using instrumental neutron activation analysis (INAA). The main objective was testing the chemical patterns previously identified using cluster and principal component analysis, forming groups of ceramics according the multivariate chemical composition. It was verified by statistical tests that the chemical elemental data was not normally distributed and did not present homogeneity of covariance matrices for different groups, as requested by principal component analysis and other multivariate techniques. The maps obtained were consistent with the patterns identified by cluster and principal component analysis, forming two chemical groups of pottery shards for each archaeological site tested. Finally, it was verified the potential of SOMs for testing if failures in underlying hypotheses of traditional multivariate techniques might be critically influencing the results and subsequent archaeological interpretation of archaeometric data.

中文翻译：

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神经网络（SOM）用于中亚亚马逊地区考古陶瓷化学元素的INAA数据

摘要人工神经网络代表了传统多元技术的替代方法，例如主成分和判别分析，这些技术依赖于有关数据的正态分布和均方差的假设。它们也可能是功能强大的工具，可以对不存在变量之间线性相关性的系统进行多变量建模，以及可视化双变量或三变量结构中的高维数据。自组织映射（SOM）是考古学研究中关注的一种特殊神经网络。可以将SOM与其他神经网络区分开来，以保留原始多元空间的拓扑特征。在这个研究中，使用工具中子活化分析（INAA）将自组织图应用于从中央亚马逊考古陶瓷中测量的化学元素的浓度数据。主要目标是测试先前使用聚类和主成分分析确定的化学模式，根据多元化学组成形成陶瓷组。根据主成分分析和其他多元技术的要求，通过统计测试证实，化学元素数据不是正态分布的，并且没有呈现出不同组的协方差矩阵的同质性。所获得的地图与通过聚类分析和主成分分析确定的模式一致，从而为每个测试的考古现场形成了两个陶瓷碎片化学组。最后，