.png){kind=icon}
Return
Output feedback neurolinearization
.png){kind=icon}
PRE
AI
DOI：10.1016/S0019-0578(00)00048-3.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Output-feedback neurolinearization as a new method in model independent input/output linearization has been developed. In this method input-output linearization is done only based on the input-output data of the system. The performance of this new method is compared to the global linearizing control (GLC) and to a conventional PI controller. The systems selected for comparison purposes are temperature control of a CSTR reactor and pH control in a neutralization process. Output feedback neurolinearization, outperforms the other controllers both in setpoint tracking and disturbance rejection. The Ether important advantage of Output Feedback Neurolinearization is the fast that it does not depend upon the process model at all. (C) 2001 Elsevier Science Ltd. All rights reserved.
Keywords:
NEURAL-NETWORKS
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6.5
Papers:
6.0K
Citations:
2.0W
Organization
No organization information available
Cited Papers
.png){kind=icon}
ShareSave
ShareSaveShareSaveGuidance on risk assessment of herbicide tolerant GM plants by the European Food Safety Authority
PREAI
ShareSaveShareSave
View more