relation: https://khub.utp.edu.my/scholars/127/
title: Meta-learning evolutionary artificial neural network for selecting flexible manufacturing systems
creator: Bhattacharya, A.
creator: Abraham, A.
creator: Grosan, C.
creator: Vasant, P.
creator: Han, S.
description: This paper proposes the application of Meta-Learning Evolutionary Artificial Neural Network (MLEANN) in selecting flexible manufacturing systems (FMS) from a group of candidate FMS's. First, multi-criteria decision-making (MCDM) methodology using an improved S-shaped membership function has been developed for finding out the 'best candidate FMS alternative' from a set of candidate-FMSs. The MCDM model trade-offs among various parameters, namely, design parameters, economic considerations, etc., affecting the FMS selection process in multi-criteria decision-making environment. Genetic algorithm is used to evolve the architecture and weights of the proposed neural network method. Further, a back-propagation (BP) algorithm is used as the local search algorithm. The selection of FMS is made according to the error output of the results found from the MCDM model. © Springer-Verlag Berlin Heidelberg 2006.
publisher: Springer Verlag
date: 2006
type: Article
type: PeerReviewed
identifier:   Bhattacharya, A. and Abraham, A. and Grosan, C. and Vasant, P. and Han, S.  (2006) Meta-learning evolutionary artificial neural network for selecting flexible manufacturing systems.  Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3973 L.  pp. 891-897.  ISSN 03029743     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-33745911288&doi=10.1007%2f11760191_130&partnerID=40&md5=54936ad14bb90f93b3ee8680f9a11d32
relation: 10.1007/11760191₁₃₀
identifier: 10.1007/11760191₁₃₀