Nonlinear PLS monitoring based on ANFIS

Hongbin Liu; Xiangyu Li; Changkyoo Yoo

doi:10.1109/CCDC.2017.7978113

Nonlinear PLS monitoring based on ANFIS

Hongbin Liu, Xiangyu Li, Changkyoo Yoo

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

An adaptive neuro-fuzzy inference system-based partial least squares (ANFIS-PLS) method was proposed for monitoring nonlinear processes. The ANFIS was used as a predictor to represent the nonlinear relationship between input and output score variables in each inner loop of PLS, and fuzzy c-means clustering was employed to determine the number of fuzzy rules. Moreover, the hybrid learning algorithm was used to update and optimize the parameters of ANFIS. To determine the confidence limits for monitoring, the non-parametric kernel density estimation method was performed. A case study on the benchmark simulation model 1 of nonlinear biological wastewater treatment processes was evaluated to demonstrate the efficient monitoring performance of the proposed method. The results show that the proposed method can give superior monitoring performance compared to the traditional principal component analysis monitoring method.

Original language	English
Title of host publication	Proceedings of the 29th Chinese Control and Decision Conference, CCDC 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	319-324
Number of pages	6
ISBN (Electronic)	9781509046560
DOIs	https://doi.org/10.1109/CCDC.2017.7978113
Publication status	Published - 12 Jul 2017
Event	29th Chinese Control and Decision Conference, CCDC 2017 - Chongqing, China Duration: 28 May 2017 → 30 May 2017

Publication series

Name	Proceedings of the 29th Chinese Control and Decision Conference, CCDC 2017

Conference

Conference	29th Chinese Control and Decision Conference, CCDC 2017
Country/Territory	China
City	Chongqing
Period	28/05/17 → 30/05/17

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

Keywords

Adaptive Neuro-Fuzzy Inference System
Kernel Density Estimation
Nonlinear Process Monitoring
Partial Least Squares
Wastewater Treatment Processes

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10.1109/CCDC.2017.7978113

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title = "Nonlinear PLS monitoring based on ANFIS",

abstract = "An adaptive neuro-fuzzy inference system-based partial least squares (ANFIS-PLS) method was proposed for monitoring nonlinear processes. The ANFIS was used as a predictor to represent the nonlinear relationship between input and output score variables in each inner loop of PLS, and fuzzy c-means clustering was employed to determine the number of fuzzy rules. Moreover, the hybrid learning algorithm was used to update and optimize the parameters of ANFIS. To determine the confidence limits for monitoring, the non-parametric kernel density estimation method was performed. A case study on the benchmark simulation model 1 of nonlinear biological wastewater treatment processes was evaluated to demonstrate the efficient monitoring performance of the proposed method. The results show that the proposed method can give superior monitoring performance compared to the traditional principal component analysis monitoring method.",

keywords = "Adaptive Neuro-Fuzzy Inference System, Kernel Density Estimation, Nonlinear Process Monitoring, Partial Least Squares, Wastewater Treatment Processes",

author = "Hongbin Liu and Xiangyu Li and Changkyoo Yoo",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 29th Chinese Control and Decision Conference, CCDC 2017 ; Conference date: 28-05-2017 Through 30-05-2017",

year = "2017",

month = jul,

day = "12",

doi = "10.1109/CCDC.2017.7978113",

language = "English",

series = "Proceedings of the 29th Chinese Control and Decision Conference, CCDC 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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booktitle = "Proceedings of the 29th Chinese Control and Decision Conference, CCDC 2017",

address = "United States",

}

Liu, H, Li, X & Yoo, C 2017, Nonlinear PLS monitoring based on ANFIS. in Proceedings of the 29th Chinese Control and Decision Conference, CCDC 2017., 7978113, Proceedings of the 29th Chinese Control and Decision Conference, CCDC 2017, Institute of Electrical and Electronics Engineers Inc., pp. 319-324, 29th Chinese Control and Decision Conference, CCDC 2017, Chongqing, China, 28/05/17. https://doi.org/10.1109/CCDC.2017.7978113

Nonlinear PLS monitoring based on ANFIS. / Liu, Hongbin; Li, Xiangyu; Yoo, Changkyoo.
Proceedings of the 29th Chinese Control and Decision Conference, CCDC 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 319-324 7978113 (Proceedings of the 29th Chinese Control and Decision Conference, CCDC 2017).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Nonlinear PLS monitoring based on ANFIS

AU - Liu, Hongbin

AU - Li, Xiangyu

AU - Yoo, Changkyoo

PY - 2017/7/12

Y1 - 2017/7/12

N2 - An adaptive neuro-fuzzy inference system-based partial least squares (ANFIS-PLS) method was proposed for monitoring nonlinear processes. The ANFIS was used as a predictor to represent the nonlinear relationship between input and output score variables in each inner loop of PLS, and fuzzy c-means clustering was employed to determine the number of fuzzy rules. Moreover, the hybrid learning algorithm was used to update and optimize the parameters of ANFIS. To determine the confidence limits for monitoring, the non-parametric kernel density estimation method was performed. A case study on the benchmark simulation model 1 of nonlinear biological wastewater treatment processes was evaluated to demonstrate the efficient monitoring performance of the proposed method. The results show that the proposed method can give superior monitoring performance compared to the traditional principal component analysis monitoring method.

AB - An adaptive neuro-fuzzy inference system-based partial least squares (ANFIS-PLS) method was proposed for monitoring nonlinear processes. The ANFIS was used as a predictor to represent the nonlinear relationship between input and output score variables in each inner loop of PLS, and fuzzy c-means clustering was employed to determine the number of fuzzy rules. Moreover, the hybrid learning algorithm was used to update and optimize the parameters of ANFIS. To determine the confidence limits for monitoring, the non-parametric kernel density estimation method was performed. A case study on the benchmark simulation model 1 of nonlinear biological wastewater treatment processes was evaluated to demonstrate the efficient monitoring performance of the proposed method. The results show that the proposed method can give superior monitoring performance compared to the traditional principal component analysis monitoring method.

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KW - Kernel Density Estimation

KW - Nonlinear Process Monitoring

KW - Partial Least Squares

KW - Wastewater Treatment Processes

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M3 - Conference contribution

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T3 - Proceedings of the 29th Chinese Control and Decision Conference, CCDC 2017

SP - 319

EP - 324

BT - Proceedings of the 29th Chinese Control and Decision Conference, CCDC 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 29th Chinese Control and Decision Conference, CCDC 2017

Y2 - 28 May 2017 through 30 May 2017

ER -

Nonlinear PLS monitoring based on ANFIS

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Keywords

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