Abstract
A hierarchically neural network (HNN) is proposed in this paper. This HNN, contains two sub-neural networks, is used to predict the chemical oxygen demand (COD) and biochemical oxygen demand (BOD) concentrations. In the model the effluent COD of wastewater treatment is taken as the input of effluent BOD. The three layered RBF neural network is used in each sub-neural network. The training algorithm of the proposed HNN is simplified through the use of an adaptive computation algorithm (ACA). Meanwhile the results of simulations demonstrate that the new neural network can predict the key parameters accurately and the proposed HNN has a better performance than some other existing networks.
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Qiao, J., Ren, D., Han, H. (2012). Soft Measurement Modeling Based on Hierarchically Neural Network (HNN) for Wastewater Treatment. In: Wang, J., Yen, G.G., Polycarpou, M.M. (eds) Advances in Neural Networks – ISNN 2012. ISNN 2012. Lecture Notes in Computer Science, vol 7368. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31362-2_63
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DOI: https://doi.org/10.1007/978-3-642-31362-2_63
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