Title :
Applying the connectionist inductive learning and logic programming system to power system diagnosis
Author :
Garcez, Artur S d´Avila ; Zaverucha, Gerson ; da Silva, Victor Navarro A L
Author_Institution :
Dept. of Comput., Imperial Coll. of Sci., Technol. & Med., London, UK
Abstract :
The connectionist inductive learning and logic programming system, C-IL2P, integrates the symbolic and connectionist paradigms of artificial intelligence through neural networks that perform massively parallel logic programming and inductive learning from examples and background knowledge. This work presents an extension of C-IL2P that allows the implementation of extended logic programs in neural networks. This extension makes C-IL2P applicable to problems where the background knowledge is represented in a default logic. As a case example, we have applied the system for fault diagnosis of a simplified power system generation plant, obtaining good preliminary results
Keywords :
diagnostic expert systems; fault diagnosis; feedforward neural nets; knowledge based systems; learning by example; logic programming; maintenance engineering; parallel programming; power system analysis computing; connectionist inductive learning; fault diagnosis; feedforward neural networks; logic programming; machine learning; massively parallel programming; power generation plant; power system diagnosis; symbolic paradigms; Artificial intelligence; Artificial neural networks; Hybrid power systems; Industrial power systems; Knowledge acquisition; Logic programming; Machine learning; Neural networks; Power system faults; Power systems;
Conference_Titel :
Neural Networks,1997., International Conference on
Conference_Location :
Houston, TX
Print_ISBN :
0-7803-4122-8
DOI :
10.1109/ICNN.1997.611649