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• W1938449992 abstract "The author describes a neural network architecture and training procedure that provide an efficient means of modeling complicated surface functions. Essentially, the technique operates by constructing surfaces in a step-wise manner out of Gaussian-shaped bumps and depressions. The rationale behind the approach is explained with reference to a surface modeling interpretation of layered feedforward networks. This is followed by a description of the training procedure, using the modeling of a cowboy-hat-shaped surface as an example problem. The advantages of the technique are that it ensures convergence on a solution to within any tolerance for a set of training patterns, converges rapidly, and circumvents the issue of how many hidden neurons to incorporate in a network. The author also presents a demonstration of how to smooth the output produced by a network and thereby improve its powers of interpolation, this time using the problem of drawing a square as an example. >" @default.
• W1938449992 created "2016-06-24" @default.
• W1938449992 creator A5007739957 @default.
• W1938449992 date "1991-01-01" @default.
• W1938449992 modified "2023-09-26" @default.
• W1938449992 title "A Gaussian-based feedforward network architecture and complementary training algorithm" @default.
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• W1938449992 doi "https://doi.org/10.1109/ijcnn.1991.170399" @default.
• W1938449992 hasPublicationYear "1991" @default.
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