General regression neural network: Difference between revisions
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'''Generalized regression neural network''' (GRNN) is a variation to radial basis [[neural networks]] ([[Radial basis function network|RBFNN]]). GRNN was suggested by D.F. Specht in 1991.<ref>{{cite web|url=http://ieeexplore.ieee.org/document/97934/ |title=A general regression neural network - IEEE Xplore Document |publisher=Ieeexplore.ieee.org |date=2002-08-06 |accessdate=2017-03-13}}</ref> It can be used for [[Regression analysis|regression]], [[prediction]], and [[classification]]. GRNN can also be a good solution for online dynamic systems. |
'''Generalized regression neural network''' (GRNN) is a variation to radial basis [[neural networks]] ([[Radial basis function network|RBFNN]]). GRNN was suggested by D.F. Specht in 1991.<ref>{{cite web|url=http://ieeexplore.ieee.org/document/97934/ |title=A general regression neural network - IEEE Xplore Document |publisher=Ieeexplore.ieee.org |date=2002-08-06 |accessdate=2017-03-13}}</ref> It can be used for [[Regression analysis|regression]], [[prediction]], and [[classification]]. GRNN can also be a good solution for online dynamic systems. |
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GRNN represnt an improved technique in the neural networks based on the [[Regression|non-paramertic regression]]. The basic idea is that every training sample will represnt a mean to radial basis [[Neuron|neuron]]. |
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==References== |
==References== |
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Revision as of 00:04, 23 March 2017
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Generalized regression neural network (GRNN) is a variation to radial basis neural networks (RBFNN). GRNN was suggested by D.F. Specht in 1991.[1] It can be used for regression, prediction, and classification. GRNN can also be a good solution for online dynamic systems.
GRNN represnt an improved technique in the neural networks based on the non-paramertic regression. The basic idea is that every training sample will represnt a mean to radial basis neuron.
References
- ^ "A general regression neural network - IEEE Xplore Document". Ieeexplore.ieee.org. 2002-08-06. Retrieved 2017-03-13.
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