A constrained-optimization approach to training neural networks for smooth function approximation and system identification

@article{Muro2008ACA,
  title={A constrained-optimization approach to training neural networks for smooth function approximation and system identification},
  author={Gianluca Di Muro and Silvia Ferrari},
  journal={2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)},
  year={2008},
  pages={2353-2359}
}
A constrained-backpropagation training technique is presented to suppress interference and preserve prior knowledge in sigmoidal neural networks, while new information is learned incrementally. The technique is based on constrained optimization, and minimizes an error function subject to a set of equality constraints derived via an algebraic training approach. As a result, sigmoidal neural networks with long term procedural memory (also known as implicit knowledge) can be obtained and trained… CONTINUE READING

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