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

中文关键词  出水总磷  自组织模糊神经网络  改进LM  奇异值分解

英文关键词  effluent total phosphorus  self-organizing fuzzy neural network  improved Levenberg-Marquardt  singular value decomposition

中文摘要

      针对污水处理过程出水总磷预测问题, 本文提出一种基于改进LM(improved Levenberg-Marquardt, ILM)学习算法和奇异值分解(singular value decomposition, SVD)的适于在线建模的自组织模糊神经网络(fuzzy neural network, FNN)预测方法. ILM-SVDFNN采用改进LM学习算法对隶属函数中心、宽度和输出权值进行训练. 在参数自适应学习的同时, 采用单边Jacobi变换实现规则层输出阵的奇异值分解, 根据奇异值定义增长和修剪指标实现规则层神经元在线动态调整. 此外, 证明了所提方法在网络结构固定和调整阶段的收敛性. 最后, 利用典型非线性系统辨识、Mackey-Glass时间序列预测和实际污水处理过程出水总磷预测实验进行验证. 仿真结果显示所设计的自组织模糊神经网络结构紧凑且预测精度较高, 较好地满足了污水处理厂对出水总磷检测精度和实时性的要求.

英文摘要

      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 TP. 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.