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A pretreatment method of wastewater based on artificial intelligence and fuzzy neural network system

机译:基于人工智能和模糊神经网络系统的废水预处理方法

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摘要

A pretreatment method of industrial saline wastewater based on Artificial Intelligence based fuzzy neural network analysis was proposed to improve the pretreatment accuracy of industrial saline wastewater. This method uses a four-layer AI fuzzy neural network model and proposes a graded fuzzy neural network model for pretreatment method of industrial saline wastewater, it includes input layer, fuzzification layer, fuzzy logical layer and output layer, and designs the framework and calculation mode of the fuzzy function block and the neural network module. Finally, the dynamic simulation experiments of dissolved oxygen control in the fifth zone and nitrate nitrogen control in the second zone are carried out based on the simulation benchmark model (BSM1) platform. The experimental results show that this approach can effectively raise the adaptive control accuracy of the system compared with PID, feed forward neural network and conventional recurrent neural network.
机译:基于人工智能的模糊神经网络分析提高了工业盐水废水预处理精度的基于人工智能的工业盐水废水的预处理方法。 该方法使用四层AI模糊神经网络模型,并提出了一种用于工业盐水废水的预处理方法的分级模糊神经网络模型,包括输入层,模糊层,模糊逻辑层和输出层,并设计框架和计算模式 模糊功能块和神经网络模块。 最后,基于模拟基准模型(BSM1)平台进行第二区中的第五区和硝酸盐氮控制中的溶解氧控制的动态模拟实验。 实验结果表明,与PID,馈电神经网络和传统的经常性神经网络相比,该方法可以有效地提高系统的自适应控制精度。

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