Abstract
In this research, the input/output data of a MIMO nonlinear system are used to create intelligent models for nonlinear systems. Multi layer perceptrons and neuro-fuzzy networks are utilized for the intelligent models. To make these models suitable for the predictive control, a variety of subtle points should be considered. Recurrent models and subtractive clustering are used in this research, and a pre-processing is applied to the columns of the raw data. Then the prepared data are used to train models. A reliable checking process is also studied. A Catalytic Continuous Stirred Tank Reactor is used as a case study. A computer model is used to gather the input data rather than a real one. Finally, the simulation is successfully performed to indicate the capabilities of the intelligent modeling method as well as the importance of the design considerations offered in this paper.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 17th World Congress, International Federation of Automatic Control, IFAC |
| Volume | 17 |
| Edition | 1 PART 1 |
| DOIs | |
| Publication status | Published - 2008 |
| Event | 17th World Congress, International Federation of Automatic Control, IFAC - Seoul, Korea, Republic of Duration: Jul 6 2008 → Jul 11 2008 |
Other
| Other | 17th World Congress, International Federation of Automatic Control, IFAC |
|---|---|
| Country | Korea, Republic of |
| City | Seoul |
| Period | 7/6/08 → 7/11/08 |
Keywords
- Identification for control
- Iterative modelling and control design
- Nonlinear system identification
ASJC Scopus subject areas
- Control and Systems Engineering