Comparison of Backpropagation and Kalman Filter-based Training for Neural Networks
Keyword(s):
This work describes and compares the backpropagation algorithm with the Extended Kalman filter, a second-order training method which can be applied to the problem of learning neural network parameters and is known to converge in only a few iterations. The algorithms are compared with respect to their effectiveness and speed of convergence using simulated data for both, a regression and a classification task.
2004 ◽
Vol 4
(3)
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pp. 3653-3667
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Keyword(s):
2021 ◽
Vol 172
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pp. 121159
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2009 ◽
Vol 21
(6)
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pp. 717-730
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