%0 Journal Article
%@ 03029743
%A Bhattacharya, A.
%A Abraham, A.
%A Grosan, C.
%A Vasant, P.
%A Han, S.
%C Chengdu
%D 2006
%F scholars:127
%I Springer Verlag
%J Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
%K Backpropagation; Decision making; Flexible manufacturing systems; Genetic algorithms; Learning systems; Membership functions; Parameter estimation, Back-propagation (BP) algorithm; Meta-Learning Evolutionary Artificial Neural Network (MLEANN); Multi-criteria decision-making; Search algorithm, Neural networks
%P 891-897
%R 10.1007/11760191₁₃₀
%T Meta-learning evolutionary artificial neural network for selecting flexible manufacturing systems
%U https://khub.utp.edu.my/scholars/127/
%V 3973 L
%X 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.
%Z 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