Nonlinear Identification Using Orthogonal Forward Regression With Nested Optimal Regularization

@article{Hong2015NonlinearIU,
  title={Nonlinear Identification Using Orthogonal Forward Regression With Nested Optimal Regularization},
  author={Xia Hong and Sheng Chen and Junbin Gao and Christopher J. Harris},
  journal={IEEE Transactions on Cybernetics},
  year={2015},
  volume={45},
  pages={2925-2936}
}
An efficient data based-modeling algorithm for nonlinear system identification is introduced for radial basis function (RBF) neural networks with the aim of maximizing generalization capability based on the concept of leave-one-out (LOO) cross validation. Each of the RBF kernels has its own kernel width parameter and the basic idea is to optimize the multiple pairs of regularization parameters and kernel widths, each of which is associated with a kernel, one at a time within the orthogonal… CONTINUE READING

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