Hybrid fuzzy modeling of chemical processes
type: Item
instance of: scholarly article
title: Hybrid fuzzy modeling of chemical processes
zbMATH Open document ID: 1027.93004
DOI: 10.1016/S0165-0114(01)00242-1
author: Yin Wang
author: Gang Rong
author: Shuqing Wang
published in: Fuzzy Sets and Systems
publication date: 2003-02-05T00:00:00Z
review text: The expert knowledge, local linear models, and historical data are combined into a uniform ``grey-box'' fuzzy modeling framework. The expert fuzzy model (EFM), local linear models (LLM), and the compensation fuzzy model (CFM) constitute the basic layer. At the second layer the special module decides which model in the basic layer should be used to make the final prediction. Its fuzzy selection rules are compatible with the understanding of engineers. If there is enough training data and the CFM is reliable then use the CFM. If the CFM is not reliable but the EFM can provide some reliable compensation, use these two models to form the hybrid fuzzy model output. If neither the EFM nor the CFM give reliable predictions, an LLM should be used as a default model. The proposed hybrid fuzzy modeling framework is applied to a simulated pH neutralization process.
Mathematics Subject Classification ID: 93A30
Mathematics Subject Classification ID: 93C42
Mathematics Subject Classification ID: 93C95
zbMATH DE Number: 1864838
zbMATH Keywords: process modeling
zbMATH Keywords: hybrid fuzzy modeling
zbMATH Keywords: local linear model
zbMATH Keywords: pH neutralization process
zbMATH Keywords: grey-box fuzzy modeling
zbMATH Keywords: expert fuzzy model
zbMATH Keywords: compensation fuzzy model
reviewed by: Ingmar Randvee
MaRDI profile type: Publication
cites work: Analysis and design for a class of complex control systems. I: Fuzzy modelling and identification
cites work: Identification of nonlinear systems using empirical data and prior knowledge -- An optimization approach
cites work: A statistical perspective on ill-posed inverse problems (with discussion)
full work available at URL: s0165-0114(01)00242-1
OpenAlex ID: W2025969982
description: scientific article; zbMATH DE number 1864838