Logistic GMDHtype neural network using principal

Authors: Tadashi Kondo Junji Ueno Shoichiro Takao

Publish Date: 2015/03/24

Volume: 20, Issue: 2, Pages: 137-144

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Abstract

The logistic group method of data handling GMDHtype neural network is proposed and applied to the medical image diagnosis of lung cancer In this logistic GMDHtype neural network algorithm the principal componentregression analysis is used to estimate the parameters of the neural network and the multicolinearity which is generated in the conventional GMDH algorithm is protected Therefore accurate and stable predicted values are obtained in the logistic GMDHtype neural network Furthermore the polynomial and logistic neurons are used and the neural network architectures are automatically organized so as to minimize the prediction error criterion defined as Akaike’s information criterion or prediction sum of squares The identification results show that the logistic GMDHtype neural network algorithm is useful for the medical image diagnosis of lung cancer since the optimum neural network architecture is automatically organized so as to fit the complexity of the medical images

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