Comments on "Constructive learning of recurrent neural networks: limitations of recurrent cascade correlation and a simple solution"

@article{Kremer1996CommentsO,
  title={Comments on "Constructive learning of recurrent neural networks: limitations of recurrent cascade correlation and a simple solution"},
  author={Stefan C. Kremer},
  journal={IEEE transactions on neural networks},
  year={1996},
  volume={7 4},
  pages={1047-51}
}
Giles et al. (1995) have proven that Fahlman's recurrent cascade correlation (RCC) architecture is not capable of realizing finite state automata that have state-cycles of length more than two under a constant input signal. This paper extends the conclusions of Giles et al. by showing that there exists a corollary to their original proof which identifies a… CONTINUE READING