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Training radial basis neural networks with the extended
Kalman filter
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@adota
مقدمه مقاله به صورت زیر است
Radial basis function (RBF) neural networks provide attractive possibilities for solving
signal processing and pattern classi$cation problems. Several algorithms have been proposed for choosing the RBF prototypes and training the network. The selection of the RBF
prototypes and the networkweights can be viewed as a system identi$cation problem. As
such, this paper proposes the use of the extended Kalman $lter for the learning procedure.
After the user chooses how many prototypes to include in the network, the Kalman $lter simultaneously solves for the prototype vectors and the weight matrix. A decoupled extended
Kalman $lter is then proposed in order to decrease the computational e3ort of the training
algorithm. Simulation results are presented on reformulated radial basis neural networks
as applied to the Iris classi$cation problem. It is shown that the use of the Kalman $lter
results in better learning than conventional RBF networks and faster learning than gradient
descent. c 2002 Elsevier Science B.V. All rights reserved.
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