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有關 Visualization of radial basis function networks. 的學術文章 | |
Visualization of radial basis function networks - Agogino - 11 個引述 … -generating hierarchical radial basis function networks - Selver - 60 個引述 Radial basis function networks-revisited - Lowe - 11 個引述 | |
Visualization of radial basis function networks
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由 A Agogino 著作1999被引用 11 次 — Presents a method for the 3D visualization of the structure of radial basis function networks. This method allows the visualization of basis function ...
Visualization of radial basis function networks
IEEE Xplore
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由 A Agogino 著作1999被引用 11 次 — This method allows the visualization of basis function characteristics (centers and width) along with second level weights. Network properties can be displayed.
Visualizing radial basis functions—ArcMap | Documentation
Esri
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Radial basis functions (RBF) enable you to create a surface that captures global trends and picks up local variation.
Visualization of the RBF network.
ResearchGate
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ResearchGate
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As presented on the visualization of the network shown in Figure 12, the first layer has radial basis transfer functions with the maximum number of 80 neurons, ...
Understanding Radial Basis Function Networks (RBFNs)
Medium
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Medium
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2024年10月13日 — A Radial Basis Function Network is a type of artificial neural network that uses radial basis functions (RBFs) as activation functions in its hidden layer.
What are Radial Basis Functions Neural Networks ...
Simplilearn.com
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2023年5月25日 — Radial Basis Function (RBF) Networks are a particular type of Artificial Neural Network used for function approximation problems.
Gradient-based training and pruning of radial basis ...
ScienceDirect.com
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ScienceDirect.com
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由 J Määttä 著作2021被引用 21 次 — We propose a fully gradient-based technique for training radial basis function networks with an efficient and scalable open-source implementation.
What are the Radial Basis Functions Neural Networks?
Analytics Vidhya
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2024年7月10日 — Radial Basis Function Neural Networks (RBFNNs) use radial basis functions for activation, excelling in pattern recognition, & interpolation.
Learning in Deep Radial Basis Function Networks
MDPI
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由 F Wurzberger 著作2024被引用 6 次 — In this paper, we show that deeper RBF architectures with multiple radial basis function layers can be designed together with efficient learning schemes.