It is well known that some key effluent quality parameters are difficult to measure online in the urban sewage treatment.To solve this problem,this paper proposes a new soft-measurement model using empirical mode decomposition and modular neural network(EMD-SMNN)for effluent quality parameters.First,a task decomposition algorithm based on EMD is proposed,which can decompose a complex,multi-frequency time series of effluent quality parameters into several sub-time series,and it can adaptively adjust subnetwork modules according to the complexity and similarity of sub-time series calculating by the sample entropy and Euclidean distance.Then,a novel self-organizing algorithm of FNN is proposed to solve the problem that the initiating structure of subnetwork is difficult to given,which can dynamically adjust the structure of subnetworks and predict subtasks effectively.Finally,through the benchmark time series prediction and the actual effluent water quality parameter detection in the sewage treatment plant,it is verified that the proposed EMD-SMNN has a good prediction accuracy and self-adaptability.
关键词
经验模态分解/动态建模/模块化神经网络/时间序列预测/废水
Key words
empirical mode decomposition/dynamic modeling/modular neural network/time series prediction/wastewater
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