PMID: 8591342Jan 1, 1995Paper

Extraction of fuzzy rules using neural networks with structure level adaptation and its application to diagnosis for hepatobiliary disorders

Medinfo
T IchimuraK Yoshida

Abstract

First, this paper presents the reasoning and the learning method for fuzzy rules using structure level adaptation of neural networks. In a usual neural network's mechanism, during learning process of rules, we can observe the following two behaviors: Case 1: If a neural network does not have enough neurons to be satisfied to infer, then the input weight vector will have a tendency to fluctuate greatly, even after a certain long period the learning process. In this case, the network needs to generate a new neuron as its parent's attribute is inherited. Case 2: If a neural network has enough neurons to infer, and even if the input weight vector of each neuron will converge to a certain value, then we shall be able to turn out unnecessary neurons from the network in the calculation. In this case, because it is necessary to delete a redundant neuron to the calculation, the neuron is annihilated without affecting the performance of the network. By observing such behaviors, we can generate or annihilate the specified neuron respectively to achieve an overall good system. In the proposed method, we described a procedure to derive the neuron generation/annihilation automatically and applied the procedure to learning system. Next, we ap...Continue Reading

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