@inproceedings{jam-acu-05-aa-neural,
  author = {Marcela Jamett and Gonzalo Acu{\~n}a},
  title = {Comparative Assessment of Interval and Affine Arithmetic in Neural Network State Prediction},
  booktitle = {Advances in Neural Networks: Proc. International Symposium on Neural Networks},
  pages = {448-453},
  year = 2005,
  series = {Lecture Notes in Computer Science},
  volume = {3497},
  doi = {10.1007/11427445_73},
  comment = {Compares IA and AA in predicting output of neural networks},
  abstract = {Two set theory methods, Interval and Affine Arithmetic, are used together with feedforward neural networks (FNN) in order to study their ability to perform state prediction in non-linear systems. Some fundamental theory showing the basic interval and affine arithmetic operations necessary to forward propagate through a FNN is presented and an application to a generic biotechnological process is performed confirming that due to the way the perturbations of the input data are considered, affine FNN perform better than interval ones.}
}