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作者:

Qiao, Junfei (Qiao, Junfei.) | Sun, Zijian (Sun, Zijian.) | Meng, Xi (Meng, Xi.)

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EI Scopus SCIE

摘要:

The accurate and timely prediction of nitrogen oxides (NOx) emissions ensures eco-friendly and efficient operations for municipal solid waste incineration (MSWI) plants. Due to the high nonlinearity and uncertainty in MSWI processes, constructing an efficient prediction model remains challenging. This article proposes a comprehensively improved interval type-2 fuzzy neural network (CI-IT2FNN) for NOx emissions prediction. First, the neighborhood rough set is introduced to determine the structure of this fuzzy neural network automatically, including the number of fuzzy rules and their corresponding consequent parameters. Second, an adaptive shape factor is added to the fuzzy membership function to better cope with the uncertainty, which can help to improve the generalization ability of network. Furthermore, to reduce the computational complexity, the Begian-Melek-Mendel method is utilized as the defuzzification method in this article. Then, by integrating the linear least square estimation and gradient decent, a hierarchical learning algorithm is applied to adjust the network parameters to further enhance the learning efficiency and accuracy. Finally, after being evaluated by a benchmark simulation, the proposed CI-IT2FNN demonstrates its effectiveness and superiority on NOx emissions prediction.

关键词:

Predictive models Incineration Shape Uncertainty Interval type-2 fuzzy neural network (IT2FNN) Nitrogen nitrogen oxides (NOx) emissions prediction municipal solid waste incineration (MSWI) neighborhood rough set (NRS) Fuzzy neural networks Waste materials

作者机构:

  • [ 1 ] [Qiao, Junfei]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 2 ] [Sun, Zijian]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 3 ] [Meng, Xi]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 4 ] [Qiao, Junfei]Beijing Key Lab Computat Intelligence & Intellige, Beijing Lab Smart Environm Protect, Beijing 100124, Peoples R China
  • [ 5 ] [Sun, Zijian]Beijing Key Lab Computat Intelligence & Intellige, Beijing Lab Smart Environm Protect, Beijing 100124, Peoples R China
  • [ 6 ] [Meng, Xi]Beijing Key Lab Computat Intelligence & Intellige, Beijing Lab Smart Environm Protect, Beijing 100124, Peoples R China
  • [ 7 ] [Qiao, Junfei]Minist Educ, Engn Res Ctr Intelligent Percept & Autonomous Con, Beijing 100124, Peoples R China
  • [ 8 ] [Sun, Zijian]Minist Educ, Engn Res Ctr Intelligent Percept & Autonomous Con, Beijing 100124, Peoples R China
  • [ 9 ] [Meng, Xi]Minist Educ, Engn Res Ctr Intelligent Percept & Autonomous Con, Beijing 100124, Peoples R China

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来源 :

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS

ISSN: 1551-3203

年份: 2023

期: 11

卷: 19

页码: 11286-11297

1 2 . 3 0 0

JCR@2022

被引次数:

WoS核心集被引频次:

SCOPUS被引频次: 11

ESI高被引论文在榜: 0 展开所有

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