DocumentCode :
3030994
Title :
Human-in-the-loop control with majority vote neural networks
Author :
Looney, Carl G. ; Tacker, Edgar C.
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., Nevada Univ., Reno, NV, USA
fYear :
1990
fDate :
4-7 Nov 1990
Firstpage :
224
Lastpage :
226
Abstract :
The specifications for many automated decision-making/aiding systems include a human censor in the loop. However, the problem of human cognitive overload that arises in highly complex situations necessitates that the human be relieved of much of the lower level data. The authors present a simple, robust neural network for self-organized learning that hierarchically recognizes successively higher objects from patterns in the input sensor signals. It is called the majority vote neural network. These objects are decoded into a situation-response frame for presentation to the human. If the human approves the frame, it goes to the command sequence generator to be decoded further into a sequence of commands to drive the required actions. Otherwise, the human must supply an alternate response codeword to the situation-response frame
Keywords :
computerised pattern recognition; knowledge based systems; learning systems; man-machine systems; neural nets; automated decision-making/aiding systems; command sequence generator; computerised pattern recognition; human censor; human cognitive; majority vote neural networks; man-machine systems; self-organized learning; situation-response frame; Aircraft; Biological neural networks; Control systems; Decoding; Humans; Neural networks; Pattern recognition; Power system modeling; Robustness; Voting;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems, Man and Cybernetics, 1990. Conference Proceedings., IEEE International Conference on
Conference_Location :
Los Angeles, CA
Print_ISBN :
0-87942-597-0
Type :
conf
DOI :
10.1109/ICSMC.1990.142097
Filename :
142097
Link To Document :
بازگشت