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Multiple neural networks modeling techniques in process control: a review
Author(s) -
Ahmad Zainal,
Mat Noor Rabiatul ′Adawiah,
Zhang Jie
Publication year - 2009
Publication title -
asia‐pacific journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.348
H-Index - 35
eISSN - 1932-2143
pISSN - 1932-2135
DOI - 10.1002/apj.213
Subject(s) - artificial neural network , robustness (evolution) , computer science , nervous system network models , generalization , artificial intelligence , process (computing) , time delay neural network , stochastic neural network , physical neural network , deep neural networks , types of artificial neural networks , nonlinear system , machine learning , mathematics , mathematical analysis , biochemistry , chemistry , physics , quantum mechanics , gene , operating system
This paper reviews new techniques to improve neural network model robustness for nonlinear process modeling and control. The focus is on multiple neural networks. Single neural networks have been dominating the neural network ‘world’. Despite many advantages that have been mentioned in the literature, some problems that can deteriorate neural network performance such as lack of generalization have been bothering researchers. Driven by this, neural network ‘world’ evolves and converges toward better representations of the modeled functions that can lead to better generalization and manages to sweep away all the glitches that have shadowed neural network applications. This evolution has lead to a new approach in applying neural networks that is called as multiple neural networks. Just recently, multiple neural networks have been broadly used in numerous applications since their performance is literally better than that of those using single neural networks in representing nonlinear systems. Copyright © 2009 Curtin University of Technology and John Wiley & Sons, Ltd.