title: Neural network training with global optimization techniques.
abstract: This paper presents an approach of using Simulated Annealing and Tabu Search for the simultaneous optimization of neural network architectures and weights. The problem considered is the odor recognition in an artificial nose. Both methods have produced networks with high classification performance and low complexity. Generalization has been improved by using the backpropagation algorithm for fine tuning. The combination of simple and traditional search methods has shown to be very suitable for generating compact and efficient networks.
authors.affiliation: Center of Informatics, Federal University of Pernambuco, Cidade Universitária, P.O. Box 7851, Recife, Pernambuco, 50.732-970, Brazil. ay@cin.ufpe.br
authors.forename: Akio
authors.initials: A
authors.lastname: Yamazaki
authors.forename: Teresa B
authors.initials: TB
authors.lastname: Ludermir
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003491:Cybernetics; D005246:Feedback; D005793:Generalization, Psychological; D006801:Humans; D016571:Neural Networks, Computer; D009666:Nose; D009812:Odorants; D011897:Random Allocation; D021641:Recognition, Psychology; D013997:Time Factors; D014894:Weights and Measures