Prediction of effluent total phosphorus based on self-organizing fuzzy neural network
Abstract：A novel online self-organizing fuzzy neural network (FNN) based on the improved Levenberg-Marquardt (ILM) learning algorithm and singular value decomposition (SVD) is proposed to predict the effluent total phosphorus (TP) in a wastewater treatment process.The centers and widths of membership functions and weights of output layer are trained by ILM learning algorithm.Meanwhile,the output matrix of the rule layer is decomposed with SVD,which is implemented by one-sided Jacobi's transformation.The neurons of rule layer are adjusted dynamically with growing and pruning algorithms,which are based on the singular values.In addition,the convergence of the proposed ILM-SVDFNN has been proved both in the structure fixed phase and the structure adjusting phase.Finally,the validity and practicability of the model are illustrated with three examples,including typical nonlinear system identification,Mackey-Glass time series prediction,and prediction of effluent TE Simulation results demonstrate that the proposed ILM-SVDFNN generates a fuzzy neural network automatically and effectively with a highly accurate and compact structure,and it can well satisfy the detection accuracy and real-time requirements of the prediction of effluent TP.
Keywords：effluent total phosphorusfuzzy neural networkself-organizing fuzzy neural networkimproved Levenberg-Marquardtsingular value decomposition
Publication Date：2017-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：9( 224-232 )