Title :
A neural network model for learning rule-based systems
Author_Institution :
Dept. of Comput. & Inf. Sci., Florida Univ., Gainesville, FL, USA
Abstract :
Knowledgetron, a novel intelligent system which derives rule-based expert systems from neural networks trained by a special computational model, is described. The knowledge of such neural networks is extracted and represented as production rules. The main consideration is that the generated rule-based system perform as well as the original neural network. The system consists of two coupled components. One is the KTBP trainer, which is applied to a multilayer neural network for learning from the data. The trained neural network is translated into a rule-based system by the second component, the KT translator. The feasibility and validity of Knowledgetron have been demonstrated on both small and large neural networks for practical applications
Keywords :
knowledge acquisition; knowledge based systems; learning systems; neural nets; KT translator; KTBP trainer; Knowledgetron; computational model; expert systems; learning rule-based systems; neural network model; production rules; Computational intelligence; Computational modeling; Computer networks; Data mining; Expert systems; Intelligent networks; Intelligent systems; Knowledge based systems; Multi-layer neural network; Neural networks;
Conference_Titel :
Neural Networks, 1992. IJCNN., International Joint Conference on
Print_ISBN :
0-7803-0559-0
DOI :
10.1109/IJCNN.1992.287188