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- W2020598133 abstract "When the input variables are not mutual independence, it is easy to arise over fitting phenomenon using neural network modeling. So it leads to not only low accuracy of the proposed model but also the long time of modeling .In this paper using immune algorithm to optimize the input variables and selection, The simulation results show that the established model after optimization improves the performance, increases the accuracy and shortens the time of the modeling. Which shows that this algorithm can be effectively reduce the dimension of input variables of neural network." @default.
- W2020598133 created "2016-06-24" @default.
- W2020598133 creator A5063022863 @default.
- W2020598133 date "2013-09-01" @default.
- W2020598133 modified "2023-09-27" @default.
- W2020598133 title "Application of Immune Algorithm on Input Variable Dimension Reduction in Neural Network Modeling" @default.
- W2020598133 cites W2075734084 @default.
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- W2020598133 doi "https://doi.org/10.4028/www.scientific.net/amr.791-793.1537" @default.
- W2020598133 hasPublicationYear "2013" @default.
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