Title :
Task decomposition and competing expert system-artificial neural net objects for reliable and real time inference
Author :
Khosla, R. ; Dillon, T.
Author_Institution :
Dept. of Comput. Sci. & Comput. Eng., La Trobe Univ., Bundoora, Vic., Australia
Abstract :
An integrated model for real time alarm processing in a real world terminal power station is applied. The integrated model is a combination of a generic neuro-expert system model, object model, and UNIX operating system process (UOSP) model. It is shown how the massive parallelism and fast execution features of ANNs help to cope with real-time system constraints like data variability and fast response time. For further enhancing reliability, a practical use of competing expert system-artificial neural networks (ES-ANN) objects is proposed
Keywords :
alarm systems; expert systems; inference mechanisms; neural nets; power engineering computing; power stations; real-time systems; UNIX operating system process; UOSP model; competing expert system-artificial neural net objects; data variability; fast execution features; fast response time; massive parallelism; neuro-expert system model; object model; reliable real-time inference; task decomposition; terminal power station; Artificial neural networks; Computer network reliability; Computer networks; Concurrent computing; Electronic switching systems; Neural networks; Power engineering computing; Power system modeling; Power system reliability; Real time systems;
Conference_Titel :
Neural Networks, 1993., IEEE International Conference on
Conference_Location :
San Francisco, CA
Print_ISBN :
0-7803-0999-5
DOI :
10.1109/ICNN.1993.298657
