Cumincad : CUMINCAD Papers : Search Results

CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design
supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD and CAAD futures

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_id	b83a
id	b83a
authors	Benoudjit A, Derix C and Coates P
year	2004
title	Human perception and space classification: The Perceptive Network
source	Proceedings of the Generative Arts conference, Milan, 2004
summary	This paper presents a computer model for space perception, and space classification that is built around two artificial neural networks (ANN). This model is the first known application in architecture, where a self-organized map (SOM) is used to create a space classification map on the base of human perception criteria. This model is built with the aim to help both the space designers (architects, interior designer and urban designers), and the space users to gain a better understanding of the space in particular, and the environment where they evolve in general. This work is the continuity of an outgoing work started in the CECA by C. Derix around Kohonen network.
keywords	neural network, self-organised feature map, perception, spatial configuration
series	other
type	normal paper
email
more	http://www.generativeart.com/
last changed	2012/09/20 21:28

_id	ecaade2007_012
id	ecaade2007_012
authors	Benoudjit, Moamed Amine; Coates, Paul S.
year	2007
title	Artificial Networks for Spatial Analysis
source	Predicting the Future [25th eCAADe Conference Proceedings / ISBN 978-0-9541183-6-5] Frankfurt am Main (Germany) 26-29 September 2007, pp. 911-918
doi	https://doi.org/10.52842/conf.ecaade.2007.911
summary	The present paper aims to present a summary of my ongoing PhD research, which is concerned with the study of the possibilities of developing an analytical and design tool based on artificial neural networks (ANN) and other Artificial Intelligence (AI) and connectionist algorithms.
keywords	Space, classification, artificial neural networks, cellular automata
series	eCAADe
email
last changed	2022/06/07 07:54

_id	ijac20076104
id	ijac20076104
authors	Benoudjit, Mohamed Amine; Coates, Paul S.
year	2008
title	Artificial networks for spatial analysis
source	International Journal of Architectural Computing vol. 6 - no. 1, pp. 59-78
summary	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.
series	journal
last changed	2008/06/18 08:12

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