Identification of a Typical CSTR Using Optimal Focused Time Lagged Recurrent Neural Network Model with Gamma Memory Filter

Applied Computational Intelligence and Soft Computing. 2009;2009 DOI 10.1155/2009/385757

 

Journal Homepage

Journal Title: Applied Computational Intelligence and Soft Computing

ISSN: 1687-9724 (Print); 1687-9732 (Online)

Publisher: Hindawi Publishing Corporation

LCC Subject Category: Science: Mathematics: Instruments and machines: Electronic computers. Computer science

Country of publisher: Egypt

Language of fulltext: English

Full-text formats available: PDF, HTML, ePUB, XML

 

AUTHORS

S. N. Naikwad (Department of Electrical Engineering, College of Engineering and Technology, Babhulgaon, Akola-444 104, India)
S. V. Dudul (Department of Applied Electronics, Faculty of Engineering and Technology, Sant Gadgebaba Amaravati University, Amravati-444 602, India)

EDITORIAL INFORMATION

Blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 21 weeks

 

Abstract | Full Text

A focused time lagged recurrent neural network (FTLR NN) with gamma memory filter is designed to learn the subtle complex dynamics of a typical CSTR process. Continuous stirred tank reactor exhibits complex nonlinear operations where reaction is exothermic. It is noticed from literature review that process control of CSTR using neuro-fuzzy systems was attempted by many, but optimal neural network model for identification of CSTR process is not yet available. As CSTR process includes temporal relationship in the input-output mappings, time lagged recurrent neural network is particularly used for identification purpose. The standard back propagation algorithm with momentum term has been proposed in this model. The various parameters like number of processing elements, number of hidden layers, training and testing percentage, learning rule and transfer function in hidden and output layer are investigated on the basis of performance measures like MSE, NMSE, and correlation coefficient on testing data set. Finally effects of different norms are tested along with variation in gamma memory filter. It is demonstrated that dynamic NN model has a remarkable system identification capability for the problems considered in this paper. Thus FTLR NN with gamma memory filter can be used to learn underlying highly nonlinear dynamics of the system, which is a major contribution of this paper.