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Arma neuron networks for modeling nonlinear dynamical systems
Author(s) -
Krishnapura Venugopal G.,
Jutan Arthur
Publication year - 1997
Publication title -
the canadian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.404
H-Index - 67
eISSN - 1939-019X
pISSN - 0008-4034
DOI - 10.1002/cjce.5450750311
Subject(s) - nonlinear system , computer science , backpropagation , artificial neural network , process (computing) , autoregressive–moving average model , transformation (genetics) , biological system , control theory (sociology) , artificial intelligence , algorithm , autoregressive model , mathematics , physics , biochemistry , chemistry , control (management) , quantum mechanics , biology , gene , econometrics , operating system
A new neuronal structure, the ARMA neuron, is proposed here. These new neurons are designed for modeling nonlinear dynamics often encountered in chemical engineering processes. They are an extension of standard neurons which are used for static process modeling. These new neurons contain internal input/output dynamic structure and can model dynamic non‐linearities in a flexible manner. A nonlinear output transformation is used here as opposed to a linear version used earlier (Krishnapura and Jutan, 1993). New algorithms for training networks comprised of the new ARMA neurons are developed using the backpropagation approach. The ARMA neurons are used to model both simulated and experimental nonlinear dynamic processes, including an industrial fluidized bed reactor.