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Issue title: Special Section: Applied Machine Learning and Management of Volatility, Uncertainty, Complexity & Ambiguity (V.U.C.A)
Guest editors: Srikanta Patnaik
Article type: Research Article
Authors: Chan, Hea; * | Nai-He, Yanb
Affiliations: [a] Department of Food and Biochemical Engineering, Yantai Vocational College, Yantai, Shandong, China | [b] Office of Quality Management, Yantai Vocational College, Yantai, Shandong, China
Correspondence: [*] Corresponding author. He Chan, Department of Food and Biochemical Engineering, Yantai Vocational College, Yantai, Shandong, China. E-mail: [email protected].
Abstract: 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.
Keywords: Similarity, fuzzy neural network, saline sewage, pretreatment
DOI: 10.3233/JIFS-179945
Journal: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 2, pp. 1711-1720, 2020
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