Soft-computing Method for Ammonia Nitrogen Prediction Based on Spiking Self-organizing RBF Neural Network

LU Chao; YANG Cuili; QIAO Junfei

doi:10.13976/j.cnki.xk.2017.0752

Volume 46 Issue 6

Dec. 2017

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INFORMATION AND CONTROL > 2017 > 46(6): 752-758. > DOI: 10.13976/j.cnki.xk.2017.0752 CSTR: 32166.14.xk.2017.0752

LU Chao, YANG Cuili, QIAO Junfei. Soft-computing Method for Ammonia Nitrogen Prediction Based on Spiking Self-organizing RBF Neural Network[J]. INFORMATION AND CONTROL, 2017, 46(6): 752-758. DOI: 10.13976/j.cnki.xk.2017.0752

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Soft-computing Method for Ammonia Nitrogen Prediction Based on Spiking Self-organizing RBF Neural Network

LU Chao^{1, 2, ,},
YANG Cuili^{1, 2},
QIAO Junfei^{1, 2}

1.
Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
2.
Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing 100124, China

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Received Date: March 23, 2017
Revised Date: June 27, 2017
Accepted Date: June 05, 2017
Available Online: December 01, 2022
Published Date: December 19, 2017

Graphical Abstract

Abstract

Abstract

In order to solve the problem of ammonia nitrogen online detection in wastewater treatment process, we propose a soft-computing method based on spiking self-organizing RBF neural network. Firstly, we select the auxiliary variables which have a great influence on the prediction of ammonia nitrogen. Secondly, we use the SSORBF neural network to establish the nonlinear relationship between main variables and predicted variables. Secondly, we take the spike mechanism and gradient descent algorithm to adjust the network structure and parameters. Finally, we apply the method to measuring the effluent NH4-N concentration in a real wastewater treatment process. Simulation results show that this method can effectively realize the online prediction of ammonia nitrogen concentration, improve the prediction accuracy and adaptive ability.

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