基于类脑模块化神经网络的污水处理过程关键出水参数软测量

针对城市污水处理过程关键出水参数难以实时检测的问题，文中提出了一种基于类脑模块化神经网络（Brain-like modular neural network，BLMNN）的关键出水参数软测量方法.首先，基于互信息和专家知识进行任务分解，分析关键出水参数的相关变量，获取各出水参数的辅助变量.其次，通过模拟大脑皮层模块化分区结构，构建软测量子模型对各水质参数进行同步测量，降低软测量模型复杂度的同时保证了其精度.最后，通过基于实际数据的仿真实验验证了所提出方法的准确性和有效性.

Soft Measurement of Key Effluent Parameters in Wastewater Treatment Process Using Brain-like Modular Neural Networks

With the goal to realize the real-time measurement of key water quality parameters in wastewater treatment process, this paper constructs a novel soft-measurement model based on the brain-like modular neural network (BLMNN). First, based on the mutation information and expert knowledge, the easy-to-measure variables which have strong correlations to the effluent water quality parameters are chosen as the model inputs. Then, simulating the modular structure of brain cortex, the effluent water parameters are measured by different sub-models, improving both the modeling accuracy and modeling speed. The simulation results based on real data verify the accuracy and effectiveness of the proposed method.