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In this study, a revised radial basis function (RBF) network is proposed and applied to the identification problems of the nonlinear system and the interactive art system. In the revised RBF network, the structural parameters such as means and variances of the radial basis functions in the neurons are determined automatically and so revised RBF network can be easily applied to the practical complex...
A revised group method of data handling (GMDH)-type neural network algorithm for medical image recognition is proposed and is applied to 3-dimensional medical image analysis of the heart. The revised GMDH-type neural network algorithm has a feedback loop and can identify the characteristics of the medical images accurately using feedback loop calculations. In this algorithm, the polynomial type and...
In this study, a feedback group method of data handling (GMDH)-type neural network algorithm using prediction error criterion for self-organization is proposed. In this algorithm, the optimum neural network architecture is automatically selected from three types of neural network architectures such as the sigmoid function type neural network, the radial basis function (RBF) type neural network and...
In this study, three dimensional medical images of the lungs and brain are recognized and extracted by artificial neural networks. The neural networks used in this paper are the conventional sigmoid function neural network trained using back propagation (BP) algorithm and radial basis function (RBF) neural network. We compared the recognition results of these neural networks and determine which neural...
A radial basis function (RBF) group method of data handling (GMDH)-type neural network algorithm proposed in this paper is applied to the medical image recognition of abdominal X-ray CT images. The optimum neural network architecture for the medical image recognition is automatically organized using RBF GMDH-type neural network algorithm and the regions of abdominal organs such as the liver, stomach...
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