Please use this identifier to cite or link to this item:
|Title:||Learning fuzzy control with hybrid symbolic, connectionist networks|
|Authors:||Romaniuk, Steve G.|
|Citation:||Romaniuk, Steve G. (1993). Learning fuzzy control with hybrid symbolic, connectionist networks. 1993 IEEE International Conference on Fuzzy Systems : 241-246. ScholarBank@NUS Repository.|
|Abstract:||The problem of deriving membership functions as a means for describing linguistic variables (for some control process) and the choice of fuzzy inference operators and connectives is at the heart of developing fuzzy control systems. To this date much of the selection process is under the control of the system engineer, and dependent on his or her ability to make the right choice for the right application. In this paper, it will be shown by means of a real world example for controlling a steam engine, how hybrid learning systems can be employed for automating the design of fuzzy controllers. Deriving the necessary linguistic variables and accompanying membership functions from raw data by use of machine learning is addressed. Finally, the viability of such a system is emphasized to act not only as a fuzzy controller, but more importantly independent of human intervention, automatically derive acceptable control strategies.|
|Source Title:||1993 IEEE International Conference on Fuzzy Systems|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Dec 7, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.