TY - JOUR SN - 03029743 PB - Springer Verlag EP - 897 AV - none N1 - cited By 4; Conference of 3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks ; Conference Date: 28 May 2006 Through 1 June 2006; Conference Code:67771 TI - Meta-learning evolutionary artificial neural network for selecting flexible manufacturing systems SP - 891 Y1 - 2006/// UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-33745911288&doi=10.1007%2f11760191_130&partnerID=40&md5=54936ad14bb90f93b3ee8680f9a11d32 JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) A1 - Bhattacharya, A. A1 - Abraham, A. A1 - Grosan, C. A1 - Vasant, P. A1 - Han, S. VL - 3973 L CY - Chengdu N2 - 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. KW - Backpropagation; Decision making; Flexible manufacturing systems; Genetic algorithms; Learning systems; Membership functions; Parameter estimation KW - Back-propagation (BP) algorithm; Meta-Learning Evolutionary Artificial Neural Network (MLEANN); Multi-criteria decision-making; Search algorithm KW - Neural networks ID - scholars127 ER -