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Neural Computing and Applications

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06.01.2021 | S.I. : Deep Neuro-Fuzzy Analytics in Smart Ecosystems

Data-driven management for fuzzy sewage treatment processes using hybrid neural computing

verfasst von: Wenru Zeng, Zhiwei Guo, Yu Shen, Ali Kashif Bashir, Keping Yu, Yasser D. Al-Otaibi, Xu Gao

Erschienen in: Neural Computing and Applications | Ausgabe 33/2023

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Abstract

With the growing public attention on sustainable development and green ecosystems, the efficient management of fuzzy sewage treatment processes (FSTPs) has been a major concern in academia. Characterized by strong abstraction and analysis abilities, data mining technologies provide a novel perspective to solve this problem. In recent years, data-driven management for FSTP has been widely investigated, resulting in a number of typical approaches. However, almost all existing technical approaches consider FSTP a unidirectional, sequential process, ignoring the bidirectional temporality caused by backflow operations. Therefore, we propose a data-driven management mechanism for FSTP based on hybrid neural computing (IM-HNC for short). This mechanism attempts to capture the bidirectional time-series features of FSTP with the aid of a bidirectional long short-term memory model, and further introduces a convolutional neural network to construct feature spaces with a stronger expression capability. Empirically, we implement a series of experiments on three datasets under different parameter settings to test the efficiency and robustness of the proposed IM-HNC. The experimental results manifest that the IM-HNC has an average performance improvement of approximately 5% compared to the baselines.

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Titel: Data-driven management for fuzzy sewage treatment processes using hybrid neural computing
verfasst von: Wenru Zeng
Zhiwei Guo
Yu Shen
Ali Kashif Bashir
Keping Yu
Yasser D. Al-Otaibi
Xu Gao
Publikationsdatum: 06.01.2021
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 33/2023
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-020-05655-3

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