基于改进的PSO和模糊RBF神经网络的MBR膜污染预测（英文）  

Prediction of MBR Membrane Pollution Based on Improved PSO and Fuzzy RBF Neural Network

作　　者：陶颖新 李春青[1] 苏华[1] TAO Ying-xin;LI Chun-qing;SU Hua(College o f Computer Science and Software,Tianjin Polytechnic University,Tianjin,China)

机构地区：[1]天津工业大学计算机科学与软件学院

出　　处：《软件》2018年第8期52-56,共5页Software

基　　金：国家自然科学基金(51378350);国家青年科学基金(50808130)

摘　　要：为了提高对MBR膜通量的预测精度,采用模糊径向基函数(RBF)神经网络建立网络预测模型,并采用改进的粒子群(PSO)算法进行优化。采用模糊推理过程与RBF神经网络所具有的函数等价性,统一系统函数。在利用改进的PSO算法对模糊RBF神经网络进行训练时,先利用改进PSO算法得到模糊RBF神经网络的初始权值和阈值,然后对其进行二次优化得到最终的权值和阈值。实验仿真结果表明:本文的这种方法,缩短了响应时间,稳态误差很小,能够与膜通量的期望值更好的拟合,更好的预测膜通量。In order to improve the prediction accuracy of MBR membrane flux, using a fuzzy Radial Basis Func-tion neural network to establish a network prediction model, and use the improved Particle Swarm Optimization （PSO） algorithm to optimize. The functional equivalence of the fuzzy inference process and the RBF neural network is used to unify the system function. When using a modified PSO algorithm to train a fuzzy RBF neural network,First, using the improved PSO algorithm to obtain the initial weights and thresholds of the fuzzy RBF neural net-work, and then perform a second optimization on them to get the final weights and thresholds. The experimental simulation results show that this method of this paper shortens the response time, has a small steady-state error, and can better fit the expected value of the membrane flux and better predict the membrane flux.

关 键 词：MBR PSO RBF 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]