%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.
%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
%D 2006
%R 10.1007/11760191₁₃₀
%O 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
%J Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
%L scholars127
%C Chengdu
%T Meta-learning evolutionary artificial neural network for selecting flexible manufacturing systems
%A A. Bhattacharya
%A A. Abraham
%A C. Grosan
%A P. Vasant
%A S. Han
%I Springer Verlag
%V 3973 L
%P 891-897