NDLI: Model identification of non linear systems using soft computing technique

Content Provider	IEEE Xplore Digital Library
Author	Sridevi, M. Prakasam, P. Sarma, P.M.
Copyright Year	2014
Description	Author affiliation: SEC, Anna Univ., Chennai, India (Sridevi, M.; Sarma, P.M.) \|\| TIET, Anna Univ., Chennai, India (Prakasam, P.)
Abstract	In Chemical and process industries modeling of non linear systems posses a major challenging task to design engineers due to multivariable process interactions. Innovative technology for process identification is on high demand. A model identification using Neural Networks and ANFIS for the nonlinear systems in series is proposed and designed using conductivity as a measured parameter and flow rate as manipulated variable. Real time experimental data of the non linear system is used to train the neural network by back propagation training algorithm and ANFIS using Matlab. The identified model using various estimators is compared with the actual process model. The error analysis was also performed. Neural Model Predictive Controller controller (NMPC) is designed to control the level. Performance of NMPC compared with traditional PID controller.
Sponsorship	IEEE India Counc. C Chapt.
Starting Page	1174
Ending Page	1178
File Size	355555
Page Count	5
File Format	PDF
e-ISBN	9781479925728
DOI	10.1109/IAdCC.2014.6779493
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-02-21
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Computational modeling Process control NMPC Predictive models Conductivity Conductivity measurement ANFIS Biological neural networks Neural
Content Type	Text
Resource Type	Article

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