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The Effect of Input sequence to nonlinear artificial neural network (NN) performance in modeling CSTR

F. D., Somanti and S., Yusup and H., Zabiri (2009) The Effect of Input sequence to nonlinear artificial neural network (NN) performance in modeling CSTR. In: 3rd International Conference on Chemical &Bioprocess Eng in conjunction with 23rd Symposium of Malaysian Chemical Engineers, 12 - 14 August , Kota Kinabalu, Sabah.

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Abstract

This paper considers the aspects of the system identification of nonlinear black box empirical models for chemical process dynamics. The core of this research is to study the effect of existing input sequence to nonlinear Artificial Neural Network applied in nonlinear dynamic system. To illustrate the practical utilization of the various types of input sequences used, NARXSP dynamic Neural Network model is applied to approximate the dynamics of a first-principles model of first order kinetic reaction in a simple Continued Stirred Tank Reactor.

Item Type:Conference or Workshop Item (Paper)
Subjects:T Technology > TP Chemical technology
Academic Subject One:Academic Department - Chemical Engineering - Advance Process Control
Departments / MOR / COE:Departments > Chemical Engineering
ID Code:10752
Deposited By: Haslinda Zabiri
Deposited On:15 Nov 2013 06:51
Last Modified:15 Nov 2013 06:51

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