Extraction of fuzzy rules using neural networks with structure level adaptation--verification to the diagnosis of hepatobiliary disorders.
This paper presents the reasoning and learning method for fuzzy rules using structure level adaptation of neural networks. In a usual neural network mechanism, we can observe some behaviors during the learning process. Based on such behaviors of neuron activity, we can generate or annihilate the specified neurons respectively in hidden layer to achieve an overall good system. In the method that we have proposed, we have used a procedure to derive the neuron generation/annihilation automatically, and applied such a procedure to the learning system where the experimental data related to hepatobiliary disorders were used. After learning by using randomly chosen data, the proposed system correctly diagnosed over 70% of cases. According to these results, we can find that fuzzy rules have some relationship with the degree of the input weight vector. As a result, we can assume that fuzzy rules for hepatobiliary disorders are extracted from this learned network.
Ichimura
T
T
Department of Control & Systems Engineering, Toin University of Yokohama, Japan.
Tazaki
E
E
Yoshida
K
K
D000465:Algorithms; D001649:Bile Duct Diseases / Q000175:diagnosis; D001653:Bile Ducts, Intrahepatic / Q000473:pathology*; D006528:Carcinoma, Hepatocellular / Q000175:diagnosis; D002769:Cholelithiasis / Q000175:diagnosis; D003936:Diagnosis, Computer-Assisted; D017143:Fuzzy Logic; D006801:Humans; D008103:Liver Cirrhosis / Q000175:diagnosis; D008107:Liver Diseases / Q000175:diagnosis*; D008108:Liver Diseases, Alcoholic / Q000175:diagnosis; D008113:Liver Neoplasms / Q000175:diagnosis; D008297:Male; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D012985:Software Design