Designing dilation-erosion perceptrons with differential evolutionary learning for air pressure forecasting

@article{Arajo2011DesigningDP,
  title={Designing dilation-erosion perceptrons with differential evolutionary learning for air pressure forecasting},
  author={Ricardo de A. Ara{\'u}jo and Adriano Lorena In{\'a}cio de Oliveira and S{\'e}rgio Soares and Silvio Romero de Lemos Meira},
  journal={The 2011 International Joint Conference on Neural Networks},
  year={2011},
  pages={595-602}
}
The dilation-erosion perceptron (DEP) is a class of hybrid artificial neurons based on framework of mathematical morphology (MM) with algebraic foundations in the complete lattice theory (CLT). A drawback arises from the gradient estimation of dilation and erosion operators into classical gradient-based learning process of the DEP model, since they are not differentiable of usual way. In this sense, we present a differential evolutionary learning process, called DEP(MDE), using a modified… CONTINUE READING