Adaptive wavelet neural network for prediction of hourly NOX and NO2 concentrations

Authors:
Zhiguo Zhang;Ye San
Affiliations:
Harbin Institute of Technology, Harbin, China;Harbin Institute of Technology, Harbin, China
Venue:
WSC '04 Proceedings of the 36th conference on Winter simulation
Year:
2004

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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.