DocumentCode :
285083
Title :
Adaptive power system control with neural networks
Author :
Lee, Dennis T. ; Sobajic, Dejan J. ; Pao, Yoh-Han
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., Case Western Reserve Univ., Cleveland, OH, USA
Volume :
2
fYear :
1992
fDate :
7-11 Jun 1992
Firstpage :
838
Abstract :
The authors present a design of a new adaptive control system and demonstrate its performance in a computer simulation of a synchronous machine control task. The design utilizes the self-organization and predictive estimation capabilities of neural-net computing. The task of real-time adaptation is carried out using an error-based online learning scheme which is implemented on a cluster-wise segmented associative memory system. The systems ability to improve its own performance is demonstrated, and the possibility of using the existing control device in a supporting mode during a preliminary phase of the controller design is presented
Keywords :
adaptive control; content-addressable storage; control system synthesis; neural nets; power system computer control; adaptive control system; cluster-wise segmented associative memory system; computer simulation; controller design; error-based online learning; neural-net computing; predictive estimation; real-time adaptation; self-organization; synchronous machine control; Adaptive control; Adaptive systems; Computer errors; Computer simulation; Control systems; Neural networks; Power system control; Programmable control; Real time systems; Synchronous machines;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1992. IJCNN., International Joint Conference on
Conference_Location :
Baltimore, MD
Print_ISBN :
0-7803-0559-0
Type :
conf
DOI :
10.1109/IJCNN.1992.226883
Filename :
226883
Link To Document :
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