基于模糊递归神经网络的污泥容积指数预测模型

Prediction of activated sludge bulking based on recurrent fuzzy neural network

污泥容积指数(SVI),一个关键的污泥沉降性能评价指标。针对污水处理过程中污泥膨胀关键水质参数污泥容积指数难以准确在线测量,且实验室取样测量方法时间久、精度低,提出了一种改进型的模糊递归神经网络(HRFNN)用来预测污泥容积指数的变化,通过在网络第三层加入含有内部变量的反馈连接来实现输出信息的反馈。实验结果表明,与其他模糊神经网络相比,该网络的规模小、精度高,处理动态信息的能力明显加强。

Sludge volume index （SVI）, a key sludge sedimentation performance evaluation index, is difficult to he obtained accurately online and the conventional approaches are time-consuming, tedious and complicated. A new recurrent fuzzy neural network （HRFNN） method is proposed in this paper to predict the evolution of the sludge volume index （SVI）. HRFNN is constructed by adding feedback connections with the internal variable in the third layer of the fuzzy neural network, so it achieves output information feedback. Finally, the results of simulation indicate the efficiency of the modeling method. And compared with other fuzzy neural networks, the scale of network can be simplified and its capability of dealing with dynamic information can be strengthened, it also has better accuracy.