Abstract
An approach is proposed for nonlinear process modeling and control. Within the framework presented here, neural networks are employed for adaptively modeling nonlinear processes, on which model predictive control techniques are based. It is very difficult to provide a satisfactory representation of dynamic responses of the nonlinear processes over a wide operating region. A simple approach is proposed in which two components are used, the first component is the usual neural network which is trained to represent the steady state relationship, the second component serves as an adaptation mechanism to adaptively represent the dynamic response. The proposed approach has been demonstrated by a case study.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
