Bibliographic Details
| Title: |
Data-driven management for fuzzy sewage treatment processes using hybrid neural computing. |
| Authors: |
Zeng, Wenru1 (AUTHOR), Guo, Zhiwei1 (AUTHOR), Shen, Yu1 (AUTHOR), Bashir, Ali Kashif2 (AUTHOR), Yu, Keping3 (AUTHOR) keping.yu@aoni.waseda.jp, Al-Otaibi, Yasser D.4 (AUTHOR), Gao, Xu1,5 (AUTHOR) hughgao@outlook.com |
| Superior Title: |
Neural Computing & Applications. Nov2023, Vol. 35 Issue 33, p23781-23794. 14p. |
| Subject Terms: |
*CONVOLUTIONAL neural networks, *DATA mining |
| 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. [ABSTRACT FROM AUTHOR] |
|
Copyright of Neural Computing & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Database: |
Academic Search Premier |
|
Full text is not displayed to guests. |
Login for full access.
|