控制理论与应用2017，Vol.34Issue(2)：224-232,9.DOI:10.7641/CTA.2017.60309
基于自组织模糊神经网络的出水总磷预测
Prediction of effluent total phosphorus based on self-organizing fuzzy neural network
摘要
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.关键词
出水总磷/模糊神经网络/自组织模糊神经网络/改进Levenberg-Marquardt/奇异值分解Key words
effluent total phosphorus/fuzzy neural network/self-organizing fuzzy neural network/improved Levenberg-Marquardt/singular value decomposition分类
信息技术与安全科学引用本文复制引用
乔俊飞,周红标..基于自组织模糊神经网络的出水总磷预测[J].控制理论与应用,2017,34(2):224-232,9.基金项目
国家杰出青年科学基金项目(61225016),国家自然科学基金重点项目(61533002)资助.Supported by National Science Foundation for Distinguished Young Scholars of China (61225016) and State Key Program of National Natural Science of China (61533002). （61225016）
