A Hybrid Fuzzy-Neural Expert System for Diagnosis
Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence. Connectionist Models. Pages 494-500.
Abstract
Fuzzy Logic, a neural network and an expert system are combined to build a hybrid diagnosis system. With this system we introduce a new approach to the acquisition of knowledge bases. Our system consists of a fuzzy expert system with a dual source knowledge base. Two sets of rules are acquired, inductively from given examples and deductively formulated by a physician. A fuzzy neural network serves to learn from sample data and allows to extract fuzzy rules for the knowledge base. The diagnosis of electroencephalograms by interpretation of graphoelements serves as visualization for our approach . Preliminary results demonstrate the promising possibilities offered by our method.
@inproceedings{ijcai1995p65,
title = {A Hybrid Fuzzy-Neural Expert System for Diagnosis},
author = {Herrmann, Christoph S.},
booktitle = {Proceedings of the Fourteenth International Joint Conference on
Artificial Intelligence, {IJCAI-95}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Chris S. Mellish},
pages = {494--500},
year = {1995},
note = {Connectionist Models},
url = {https://ijcai.org/proceedings/1995/65},
}
TY - CPAPER TI - A Hybrid Fuzzy-Neural Expert System for Diagnosis AU - Herrmann, Christoph S. T2 - Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, IJCAI-95 PB - International Joint Conferences on Artificial Intelligence PY - 1995 SP - 494 EP - 500 UR - https://ijcai.org/proceedings/1995/65 ER -Copied
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