DocumentCode :
3248350
Title :
A general explanation and interrogation system for neural networks
Author :
Gilstrap ; Dominy
Author_Institution :
Comput. Sci. Corp., Beltsville, MD, USA
fYear :
1989
fDate :
0-0 1989
Abstract :
Summary form only given, as follows. The information in a trained neural network is stored as numerical weights in the neural elements and the connectivity pattern of the network. For many applications, it is desirable to have this neural network information converted into symbolic knowledge form for communication with human or machine experts. Techniques are presented for converting the information in a trained network into symbolic form as a set of rules and for obtaining explanations from the network for specific inputs. These two techniques provide the neurocomputer with one advantage of expert systems while retaining the learning and generalization capability of the neural network.<>
Keywords :
expert systems; explanation; neural nets; connectivity pattern; expert systems; explanation; generalization capability; interrogation system; learning; neural networks; neurocomputer; numerical weights; symbolic knowledge form; Expert systems; Explanation; Neural networks;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1989. IJCNN., International Joint Conference on
Conference_Location :
Washington, DC, USA
Type :
conf
DOI :
10.1109/IJCNN.1989.118383
Filename :
118383
Link To Document :
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