.png){kind=icon}
.png){kind=icon}
Return
Neural network architecture for process control based on the RTRL algorithm
.png){kind=icon}
PRE
AI
DOI：10.1002/aic.690420218.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Neural-network-based control schemes are generally designed by replacing standard elements of the classic control schemes by feedforward neural networks. The introduction of discrete time recurrent networks, which are inherently dynamic systems, into those schemes can simplify the design of neural controllers. The concept of applying recurrent networks in indirect adaptive control schemes is described. A combined network cluster consisting of the control network and the model network is constructed to allow the use of the real-time recurrent learning algorithm. To demonstrate the feasibility of the method two simulation examples are presented.
Keywords:
DIAGNOSIS
SYSTEMS
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4
Papers:
1.1W
Citations:
2.9W
Organization
No organization information available