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

Li Jia; Kai Yuan

doi:10.1016/j.cjche.2015.11.010

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

第25届中国过程控制会议专栏 | Updated：2026-01-06

- Auxiliary error and probability density function based neuro-fuzzymodel and its application in batch processes
- Chinese Journal of Chemical Engineering Vol. 23, Issue 12, Pages: 2013-2019(2015)
- Affiliations：
  
  Shanghai Key Laboratory of Power Station Automation Technology, Department of Automation, College ofMechatronics Engineering and Automation, ShanghaiUniversity,Shanghai,China,200072
- Author bio：
- Funds：
- DOI：10.1016/j.cjche.2015.11.010
  CLC：
- Published：2015
- Accepted：
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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.
DOI：

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. DOI： 10.1016/j.cjche.2015.11.010.

摘要

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.

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