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Abstract

This paper tests digital representation techniques which can be used by artificial neural networks in a computer-aided design (CAD) environment to analyze and classify architectural spaces. We developed two techniques for encoding volumetric data: vertex representation and feature space representation, as input for artificial neural networks. We tested how two different kinds of artificial neural networks, perceptron networks and self-organizing maps, could recognize given shapes in these representational formats. We have found that a one-layer perceptron can be used to classify shapes even when presented with input vectors composed of real numbers. These spatial representation techniques provide a method for using ANNs for architectural purposes.

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