Knowledge and data-driven hybrid system for modeling fuzzy wastewater treatment process-Reference-Cited by-同舟云学术

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Knowledge and data-driven hybrid system for modeling fuzzy wastewater treatment process

Published:2021-10-25 Issue: Volume: Page:
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Cheng Xuhong,Guo Zhiwei,Shen Yu,Yu Keping,Gao Xu

Funder

National Key Research and Development Program of China

Science and Technology Research Project of Chongqing Municipal Education Commission

Natural Science Foundation of Chongqing

Chongqing University Innovation Group Project

Project of Chongqing Technology and Business University

Japan Society for the Promotion of Science (JSPS) Grants-in-Aid for Scientific Research

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

https://link.springer.com/content/pdf/10.1007/s00521-021-06499-1.pdf

Reference53 articles.

1. Ansari M, Othman F, El-Shafie A (2020) Optimized fuzzy inference system to enhance prediction accuracy for influent characteristics of a sewage treatment plant. Sci Total Environ 722:137878. https://doi.org/10.1016/j.scitotenv.2020.137878

2. Chiu SLH, Lo IMC (2018) Identifying key process parameters for uncertainty propagation in environmental life cycle assessment for sewage sludge and food waste treatment. J Clean Prod 174:966–976. https://doi.org/10.1016/j.jclepro.2017.10.164

3. Liu J, Huang L, Buyukada M, Evrendilek F (2017) Response surface optimization, modeling and uncertainty analysis of mass loss response of co-combustion of sewage sludge and water hyacinth. Appl Therm Eng 125:328–335. https://doi.org/10.1016/j.applthermaleng.2017.07.008

4. Zhen L, Zhang Y et al (2021) Early collision detection for massive random access in satellite-based Internet of Things. IEEE Trans Veh Technol 70(5):5184–5189. https://doi.org/10.1109/TVT.2021.3076015

5. Guo Z, Yu K et al (2021) Deep learning-embedded social internet of things for ambiguity-aware social recommendations. IEEE Trans Netw Sci Eng. https://doi.org/10.1109/TNSE.2021.3049262

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