Meta-learning evolutionary artificial neural network for selecting flexible manufacturing systems

Bhattacharya, Arijit ORCID: https://orcid.org/0000-0001-5698-297X, Abraham, Ajith, Grosan, Crina, Vasant, Pandian and Han, Sangyong (2006) Meta-learning evolutionary artificial neural network for selecting flexible manufacturing systems. In: Advances in Neural Networks - ISNN 2006. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) . Springer-Verlag Berlin Heidelberg, CHN, pp. 891-897. ISBN 3540344829

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Abstract

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.

Item Type:	Book Section
Uncontrolled Keywords:	theoretical computer science,computer science(all) ,/dk/atira/pure/subjectarea/asjc/2600/2614
Related URLs:	http://www.scopus.com/inward/record.url?...
Depositing User:	LivePure Connector
Date Deposited:	05 May 2020 00:12
Last Modified:	22 Oct 2022 23:52
URI:	https://ueaeprints.uea.ac.uk/id/eprint/75011
DOI:	10.1007/11760191_130

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