Gradient radial basis function networks for nonlinear and nonstationary time series prediction

@article{Siong1996GradientRB,
  title={Gradient radial basis function networks for nonlinear and nonstationary time series prediction},
  author={Chng Eng Siong and Sheng Chen and Bernard Mulgrew},
  journal={IEEE transactions on neural networks},
  year={1996},
  volume={7 1},
  pages={190-4}
}
We present a method of modifying the structure of radial basis function (RBF) network to work with nonstationary series that exhibit homogeneous nonstationary behavior. In the original RBF network, the hidden node's function is to sense the trajectory of the time series and to respond when there is a strong correlation between the input pattern and the hidden node's center. This type of response, however, is highly sensitive to changes in the level and trend of the time series. To counter these… CONTINUE READING

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