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 |
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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 |
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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