Comparing BP and ART II neural network classifiers for facility location

Abstract This paper compares the performance of Artificial Neural Networks (ANNs) as classifiers in the facility location domain. The ART II (Adaptive Resonance Theory) and BP (Back Propagation) paradigms are used as exemplars of ANNs developed using supervised and unsupervised learning. Their performances are compared with that obtained using a linear multi-attribute utility model (MAUM) to classify the 48 states in the continental U.S.A. based on location profiles developed from government publications. In this paper, the models are used to classify the U.S. states based on their suitability for accommodating new manufacturing facilities. For this data set, the BP ANN model displayed robust performance and showed better convergence with the MAUM.

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