A Review of Heuristic Global Optimization Based Artificial Neural Network Training Approahes

@article{Amali2017ARO,
  title={A Review of Heuristic Global Optimization Based Artificial Neural Network Training Approahes},
  author={D. Geraldine Bessie Amali and M. Dinakaran},
  journal={IAES International Journal of Artificial Intelligence},
  year={2017},
  volume={6},
  pages={26-32}
}
  • D. Amali, M. Dinakaran
  • Published 1 March 2017
  • Computer Science
  • IAES International Journal of Artificial Intelligence
Artificial Neural Networks have earned popularity in recent years because of their ability to approximate nonlinear functions. Training a neural network involves minimizing the mean square error between the target and network output. The error surface is nonconvex and highly multimodal. Finding the minimum of a multimodal function is a NP complete problem and cannot be solved completely. Thus application of heuristic global optimization algorithms that computes a good global minimum to neural… 

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