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- W2118276635 abstract "Shows results of using simulated annealing for optimizing neural network architectures and weights. The algorithm generates networks with good generalization performance (mean classification error of 5.28%) and low complexity (mean number of connections of 11.68 out of 36) for an odor recognition task in an artificial nose." @default.
- W2118276635 created "2016-06-24" @default.
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- W2118276635 date "2003-06-25" @default.
- W2118276635 modified "2023-09-25" @default.
- W2118276635 title "Optimization of neural network weights and architectures for odor recognition using simulated annealing" @default.
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- W2118276635 doi "https://doi.org/10.1109/ijcnn.2002.1005531" @default.
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