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Volume 5 Issue 5
Aug.  2018

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2018  >  5(5): 968-976
Junfei Qiao and Hongbiao Zhou, "Modeling of Energy Consumption and Effluent Quality Using Density Peaks-based Adaptive Fuzzy Neural Network," IEEE/CAA J. Autom. Sinica, vol. 5, no. 5, pp. 968-976, Sept. 2018. doi: 10.1109/JAS.2018.7511168
Citation: Junfei Qiao and Hongbiao Zhou, "Modeling of Energy Consumption and Effluent Quality Using Density Peaks-based Adaptive Fuzzy Neural Network," IEEE/CAA J. Autom. Sinica, vol. 5, no. 5, pp. 968-976, Sept. 2018. doi: 10.1109/JAS.2018.7511168
shu

Modeling of Energy Consumption and Effluent Quality Using Density Peaks-based Adaptive Fuzzy Neural Network

doi: 10.1109/JAS.2018.7511168

  • Beijing University of Technology and Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing 100124, China

Funds:

the National Science Foundation for Distinguished Young Scholars of China 61225016

the State Key Program of National Natural Science of China 61533002

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    Abstract
  • Modeling of energy consumption (EC) and effluent quality (EQ) are very essential problems that need to be solved for the multiobjective optimal control in the wastewater treatment process (WWTP). To address this issue, a density peaks-based adaptive fuzzy neural network (DP-AFNN) is proposed in this study. To obtain suitable fuzzy rules, a DP-based clustering method is applied to fit the cluster centers to process nonlinearity. The parameters of the extracted fuzzy rules are fine-tuned based on the improved Levenberg-Marquardt algorithm during the training process. Furthermore, the analysis of convergence is performed to guarantee the successful application of the DPAFNN. Finally, the proposed DP-AFNN is utilized to develop the models of EC and EQ in the WWTP. The experimental results show that the proposed DP-AFNN can achieve fast convergence speed and high prediction accuracy in comparison with some existing methods.

     

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