A Radial Basis Function network training algorithm using a non-symmetric partition of the input space - Application to a Model Predictive Control configuration

@article{Alexandridis2011ARB,
  title={A Radial Basis Function network training algorithm using a non-symmetric partition of the input space - Application to a Model Predictive Control configuration},
  author={Alex Alexandridis and Haralambos Sarimveis and Konstantinos Ninos},
  journal={Advances in Engineering Software},
  year={2011},
  volume={42},
  pages={830-837}
}
This work presents the non-symmetric fuzzy means algorithm which is a new methodology for training Radial Basis Function neural network models. The method is based on a non-symmetric fuzzy partition of the space of input variables which results to networks with smaller structures and better approximation capabilities compared to other state-of-the-art training procedures. The lower modeling error and the smaller size of the produced models become particularly important when they are used in… CONTINUE READING

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Nonlinear adaptive model predictive control based on self-correcting neural network models

  • A Alexandridis, H. Sarimveis
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