ANFIS Identification Model of an Advanced Process Control (APC) Pilot Plant
Fuzzy Inference System structured in form of adaptive networks is an intelligent technique being used for modeling not only linear systems but also for ill-conditioned systems. Adaptive Network Based Fuzzy Inference System (ANFIS) uses a hybrid computational algorithm for modeling systems. This pap...
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my.utp.eprints.62802017-01-19T08:23:57Z ANFIS Identification Model of an Advanced Process Control (APC) Pilot Plant Baloch, M A Ismail, Idris Hanif, NHHM Baloch , Taj Mohamad TK Electrical engineering. Electronics Nuclear engineering Fuzzy Inference System structured in form of adaptive networks is an intelligent technique being used for modeling not only linear systems but also for ill-conditioned systems. Adaptive Network Based Fuzzy Inference System (ANFIS) uses a hybrid computational algorithm for modeling systems. This paper discusses the system identification model developed for an Advanced Process Control (APC) pilot plant (continuous binary distillation column) located in APC laboratory of Universiti Teknologi PETRONAS, Malaysia, using ANFIS technique. Estimation and validation of the models was performed using the experimental data collected from the pilot plant. The developed model has been validated using the best fit criteria against the measured data of the pilot plant. The result shows that the Multi Input Single Output (MISO)ANFIS model developed is capable of modeling the non-linear APC plant by means of the input-output pairs obtained from the plant experiment. 2010-06-17 Conference or Workshop Item PeerReviewed application/pdf http://eprints.utp.edu.my/6280/1/stamp.jsp%3Ftp%3D%26arnumber%3D5716224%26tag%3D1 Baloch, M A and Ismail, Idris and Hanif, NHHM and Baloch , Taj Mohamad (2010) ANFIS Identification Model of an Advanced Process Control (APC) Pilot Plant. In: International Conference on Intelligent and Advanced Systems (ICIAS 2010), 15-17 June, 2010, Kuala Lumpur, Malaysia. http://eprints.utp.edu.my/6280/ |
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TK Electrical engineering. Electronics Nuclear engineering Baloch, M A Ismail, Idris Hanif, NHHM Baloch , Taj Mohamad ANFIS Identification Model of an Advanced Process Control (APC) Pilot Plant |
description |
Fuzzy Inference System structured in form of adaptive networks is an intelligent technique being used for
modeling not only linear systems but also for ill-conditioned systems. Adaptive Network Based Fuzzy Inference System (ANFIS) uses a hybrid computational algorithm for modeling systems. This paper discusses the system identification model developed for an Advanced Process Control (APC) pilot plant (continuous binary distillation column) located in APC laboratory of Universiti Teknologi PETRONAS, Malaysia, using ANFIS technique. Estimation and validation of the models was performed using the experimental data collected from the pilot plant. The developed model has been validated using the best fit criteria against the measured data of the pilot plant. The result shows that the Multi Input Single Output (MISO)ANFIS model developed is capable of modeling the non-linear APC plant by means of the input-output pairs obtained from the plant experiment. |
format |
Conference or Workshop Item |
author |
Baloch, M A Ismail, Idris Hanif, NHHM Baloch , Taj Mohamad |
author_facet |
Baloch, M A Ismail, Idris Hanif, NHHM Baloch , Taj Mohamad |
author_sort |
Baloch, M A |
title |
ANFIS Identification Model of an Advanced Process Control (APC) Pilot Plant |
title_short |
ANFIS Identification Model of an Advanced Process Control (APC) Pilot Plant |
title_full |
ANFIS Identification Model of an Advanced Process Control (APC) Pilot Plant |
title_fullStr |
ANFIS Identification Model of an Advanced Process Control (APC) Pilot Plant |
title_full_unstemmed |
ANFIS Identification Model of an Advanced Process Control (APC) Pilot Plant |
title_sort |
anfis identification model of an advanced process control (apc) pilot plant |
publishDate |
2010 |
url |
http://eprints.utp.edu.my/6280/1/stamp.jsp%3Ftp%3D%26arnumber%3D5716224%26tag%3D1 http://eprints.utp.edu.my/6280/ |
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