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- W2384560452 abstract "A new method is proposed for training radial basis function networks (RBFN) using the unscented Kalman filter (UKF). In contrast to the extended Kalman filter (EKF) and the dual extended Kalman filter (DEKF), which extend the nonlinear functions using a first order approximation, the UKF uses a second order approximation. The most important consequence is that the algorithm does not require the Jacobi matrix of the system to be calculated, thus reducing the calculation complexity and resulting in considerable savings in time. Simulation results in the fields of chaotic time series prediction and classification problems demonstrate both the validity and faster speed of the proposed method." @default.
- W2384560452 created "2016-06-24" @default.
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- W2384560452 date "2007-01-01" @default.
- W2384560452 modified "2023-09-25" @default.
- W2384560452 title "Training radial basis neural networks with the unscented Kalman filter" @default.
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