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The radial basis function RBF network has its foundation in the conventional approximation theory. It has the capability of universal approximation. The RBF network is a popular alternative to the well-known multilayer perceptron MLP , since it has a simpler structure and a much faster training process. In this paper, we give a comprehensive survey on the RBF network and its learning. Many aspects associated with the RBF network, such as network structure, universal approximation capability, radial basis functions, RBF network learning, structure optimization, normalized RBF networks, application to dynamic system modeling, and nonlinear complex-valued signal processing, are described. We also compare the features and capability of the two models.

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Atomically precise metal nanoclusters (NCs) have recently emerged as a pivotal sector of metal nanomaterials due to unique atomic stacking mode, quantum confinement effect and abundant catalytically active sites. In...

Paraquat poisoning has become a serious public health problem in some Asian countries because of misuse or suicide. We sought to develop and validate a radiomics nomogram incorporating radiomics signature and laboratory bio-markers, for differentiating bacterial pneumonia and acute paraquat lung injury. 180 patients with pneumonia and acute paraquat who underwent CT examinations between December 2014 and October 2017 were retrospectively evaluated for testing and validation. Clinical information including demographic data, clinical symptoms and laboratory test were also recorded. A prediction model was built by using backward logistic regression and presented on a nomogram. The radiomics-based features yielded areas under the receiver operating characteristic curve of 0.870 (95% CI 0.757–0.894), sensitivity of 0.857, specificity of 0.804, positive predictive value of 83.3%, negative predictive value of 0.818 in the primary cohort, while in the validation cohort the model showed similar results (0.865 (95% CI 0.686–0.907), 0.833, 0.792, 81.5%, respectively). The individualized nomogram included radiomics signature, body temperature, nausea and vomiting, and aspartate transaminase. We have developed a radiomics nomogram that combination of the radiomics features and clinical risk factors to differentiate paraquat lung injury and pneumonia for patients with an unclear medical history of exposure to paraquat poisoning, providing appropriate therapy decision support.

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