Intelligent modeling and control of a conveyor belt grain dryer using a simplified type 2 neuro-fuzzy controller
In this article, a nonlinear autoregressive with exogenous input (NARX) network was utilized to model a conveyor belt grain dryer using a set of input–output data collected during an experiment to dry paddy grains. The resulting NARX model has achieved a remarkable modeling accuracy compared to othe...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis
2015
|
| Online Access: | http://psasir.upm.edu.my/id/eprint/782/1/Intelligent%20modeling%20and%20control%20of%20a%20conveyor%20belt%20grain%20dryer%20using%20a%20simplified%20type%202%20neuro-fuzzy%20controller.pdf http://psasir.upm.edu.my/id/eprint/782/ https://www.tandfonline.com/doi/abs/10.1080/07373937.2015.1021007?journalCode=ldrt20 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | In this article, a nonlinear autoregressive with exogenous input (NARX) network was utilized to model a conveyor belt grain dryer using a set of input–output data collected during an experiment to dry paddy grains. The resulting NARX model has achieved a remarkable modeling accuracy compared to other previously reported modeling techniques. To control the considered dryer, a simplified type 2 adaptive neuro-fuzzy inference system (ANFIS) controller was proposed. The effectiveness of this controller was demonstrated by several performance tests conducted by computer simulations. Moreover, a comparative study with other related controllers further confirmed the superiority of the proposed dryer controller. |
|---|