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...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Conference or Workshop Item |
| Published: |
2010
|
| Subjects: | |
| Online Access: | http://eprints.utp.edu.my/6280/1/stamp.jsp%3Ftp%3D%26arnumber%3D5716224%26tag%3D1 http://eprints.utp.edu.my/6280/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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. |
|---|