Predicting Water Quality With Nonstationarity: Event-Triggered Deep Fuzzy Neural Network-Reference-Cited by-同舟云学术

Predicting Water Quality With Nonstationarity: Event-Triggered Deep Fuzzy Neural Network

Published:2024-05 Issue:5 Volume:32 Page:2690-2699
ISSN:1063-6706
Container-title:IEEE Transactions on Fuzzy Systems
language:
Short-container-title:IEEE Trans. Fuzzy Syst.

Author:

Wang Gongming¹^ORCID,Chen Hong²^ORCID,Han Honggui¹^ORCID,Bi Jing³^ORCID,Qiao Junfei¹^ORCID,Tirkolaee Erfan Babaee⁴^ORCID

Affiliation:

1. Beijing Laboratory of Smart Environmental Protection, Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing, China

2. Lvzhiyuan Environmental Group, Rizhao, China

3. Faculty of Information Technology, Beijing University of Technology, Beijing, China

4. Department of Industrial Engineering, Istinye University, Istanbul, Türkiye

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Beijing Natural Science Foundation

Beijing Postdoctoral Research Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/91/10517720/10415476.pdf?arnumber=10415476

Reference41 articles.

1. Design of Syncretic Fuzzy-Neural Control for WWTP

2. An Efficient Self-Organizing Deep Fuzzy Neural Network for Nonlinear System Modeling

3. Nonlinear systems modelling based on self-organizing fuzzy neural network with hierarchical pruning scheme

4. Water Cycle Algorithm Tuned Fuzzy Expert System for Trusted Routing in Smart Grid Communication Network

5. Deep fuzzy mapping nonparametric model for real-time demand estimation in water distribution systems: A new perspective

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