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. © 1995.
Recommended Citation
C. O. Benjamin et al., "Comparing BP and ARt II Neural Network Classifiers for Facility Location," Computers and Industrial Engineering, vol. 28, no. 1, pp. 43 - 50, Elsevier, Jan 1995.
The definitive version is available at https://doi.org/10.1016/0360-8352(94)00021-E
Department(s)
Engineering Management and Systems Engineering
Second Department
Psychological Science
International Standard Serial Number (ISSN)
0360-8352
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Elsevier, All rights reserved.
Publication Date
01 Jan 1995
