Meta-learning evolutionary artificial neural network for selecting flexible manufacturing systems
- Authors
- Bhattacharya, Arijit; Abraham, Ajith; Grosan, Crina; Vasant, Pandian; Han, Sangyong
- Issue Date
- 2006
- Publisher
- SPRINGER-VERLAG BERLIN
- Citation
- ADVANCES IN NEURAL NETWORKS - ISNN 2006, PT 3, PROCEEDINGS, v.3973, pp 891 - 897
- Pages
- 7
- Journal Title
- ADVANCES IN NEURAL NETWORKS - ISNN 2006, PT 3, PROCEEDINGS
- Volume
- 3973
- Start Page
- 891
- End Page
- 897
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/65462
- DOI
- 10.1007/11760191_130
- ISSN
- 0302-9743
1611-3349
- 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.
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Collections - College of Software > School of Computer Science and Engineering > 1. Journal Articles
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