Auxiliary error and probability density function based neuro-fuzzymodel and its application in batch processes

doi:10.1016/j.cjche.2015.11.010

Chinese Journal of Chemical Engineering ›› 2015, Vol. 23 ›› Issue (12): 2013-2019.DOI: 10.1016/j.cjche.2015.11.010

• 第25届中国过程控制会议专栏 • 上一篇下一篇

Auxiliary error and probability density function based neuro-fuzzymodel and its application in batch processes

Li Jia, Kai Yuan

Shanghai Key Laboratory of Power Station Automation Technology, Department of Automation, College ofMechatronics Engineering and Automation, ShanghaiUniversity, Shanghai 200072, China

收稿日期:2015-05-19 修回日期:2015-07-10 出版日期:2015-12-28 发布日期:2016-01-19
通讯作者: Li Jia
基金资助:
Supported by the National Natural Science Foundation of China (61374044), Shanghai Science Technology Commission (12510709400), Shanghai Municipal Education Commission (14ZZ088), and Shanghai Talent Development Plan.

Auxiliary error and probability density function based neuro-fuzzymodel and its application in batch processes

Li Jia, Kai Yuan

Shanghai Key Laboratory of Power Station Automation Technology, Department of Automation, College ofMechatronics Engineering and Automation, ShanghaiUniversity, Shanghai 200072, China

Received:2015-05-19 Revised:2015-07-10 Online:2015-12-28 Published:2016-01-19
Contact: Li Jia
Supported by:
Supported by the National Natural Science Foundation of China (61374044), Shanghai Science Technology Commission (12510709400), Shanghai Municipal Education Commission (14ZZ088), and Shanghai Talent Development Plan.

摘要/Abstract

摘要： This paper focuses on resolving the identification problemof a neuro-fuzzymodel (NFM) applied in batch processes. A hybrid learning algorithm is introduced to identify the proposed NFM with the idea of auxiliary error model and the identification principle based on the probability density function (PDF). The main contribution is that the NFM parameter updating approach is transformed into the shape control for the PDF ofmodeling error.More specifically, a virtual adaptive control systemis constructed with the aid of the auxiliary errormodel and then the PDF shape control idea is used to tune NFM parameters so that the PDF of modeling error is controlled to follow a targeted PDF, which is in Gaussian or uniform distribution. Examples are used to validate the applicability of the proposed method and comparisons are made with the minimum mean square error based approaches.

关键词: Batch process, Auxiliary error model, Probability density function, Neuro-fuzzy model

Abstract: This paper focuses on resolving the identification problemof a neuro-fuzzymodel (NFM) applied in batch processes. A hybrid learning algorithm is introduced to identify the proposed NFM with the idea of auxiliary error model and the identification principle based on the probability density function (PDF). The main contribution is that the NFM parameter updating approach is transformed into the shape control for the PDF ofmodeling error.More specifically, a virtual adaptive control systemis constructed with the aid of the auxiliary errormodel and then the PDF shape control idea is used to tune NFM parameters so that the PDF of modeling error is controlled to follow a targeted PDF, which is in Gaussian or uniform distribution. Examples are used to validate the applicability of the proposed method and comparisons are made with the minimum mean square error based approaches.

Key words: Batch process, Auxiliary error model, Probability density function, Neuro-fuzzy model

Li Jia, Kai Yuan. Auxiliary error and probability density function based neuro-fuzzymodel and its application in batch processes[J]. Chinese Journal of Chemical Engineering, 2015, 23(12): 2013-2019.

Li Jia, Kai Yuan. Auxiliary error and probability density function based neuro-fuzzymodel and its application in batch processes[J]. Chin.J.Chem.Eng., 2015, 23(12): 2013-2019.

参考文献

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Auxiliary error and probability density function based neuro-fuzzymodel and its application in batch processes

Auxiliary error and probability density function based neuro-fuzzymodel and its application in batch processes

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