Toward an optimal PRNN-based nonlinear predictor

@article{Mandic1999TowardAO,
  title={Toward an optimal PRNN-based nonlinear predictor},
  author={Danilo P. Mandic and Jonathon A. Chambers},
  journal={IEEE transactions on neural networks},
  year={1999},
  volume={10 6},
  pages={1435-42}
}
We present an approach for selecting optimal parameters for the pipelined recurrent neural network (PRNN) in the paradigm of nonlinear and nonstationary signal prediction. Although there has recently been progress in terms of algorithms for training the PRNN, no account has been made of some inherent features of the PRNN. We therefore provide a study of the role of nesting, which is inherent to the PRNN architecture. The corresponding number of nested modules needed for a certain prediction… CONTINUE READING

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Nonlinear prediction of speech with a pipelined recurrent neural network and advanced learning algorithms

  • D. P. Mandic, J. Baltersee, J. A. Chambers
  • Signal Analysis and Prediction, A. Prochazka, J…
  • 1998
Highly Influential
8 Excerpts

A nonlinear adaptive predictor realized via recurrent neural networks with annealing

  • D. P. Mandic, J. A. Chambers
  • Dig. Inst. Elect. Eng. Colloquium Statist. Signal…
  • 1999
2 Excerpts

Relationship between the slope of the activation function and the learning rate for the RNN

  • D. P. Mandic, J. A. Chambers
  • Neural Comput., vol. 11, no. 5, pp. 1069–1077…
  • 1999
1 Excerpt

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