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• W2106750848 abstract "Adaptive neural network is a powerful tool for prediction of air pollution abatement scenarios. But it is often difficult to avoid overfit during the training of adaptive neural network. In this paper, based on the wavelet theory, a new algorithm is proposed to improve the generalization of adaptive neural network during on-line learning. The new algorithm trains adaptive wavelet neural network to model hourly NOx and NO2 concentrations of variance of emission sources. Results show that the new algorithm improves the generalization and the convergence velocity of adaptive wavelet neural network during on-line learning. The simulations also illustrate that adaptive wavelet neural network is capable of resolving variance of emission sources." @default.
• W2106750848 created "2016-06-24" @default.
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• W2106750848 date "2004-12-05" @default.
• W2106750848 modified "2023-09-23" @default.
• W2106750848 title "Adaptive wavelet neural network for prediction of hourly NO/sub X/ and NO/sub 2/ concentrations" @default.
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• W2106750848 doi "https://doi.org/10.5555/1161734.1162064" @default.
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