Adaptive Neuro-Fuzzy Inference Systems vs. Stochastic Models for Mortality Data

  • Valeria D’Amato
  • Gabriella Piscopo
  • Maria Russolillo
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 26)


A comparative analysis is done between stochastic models and Adaptive Neuro–Fuzzy Inference System applied to the projection of the longevity trend. The stochastic models provides the heuristic rule for obtaining projections. In the context of ANFIS models, the fuzzy logic allows for determining the learning algorithm on the basis of the relationship between inputs and outputs. In other words the rule is here deducted by the actual mortality data, because this allows for fuzzy systems to learn from the data they are modelling. This is possible by computing the membership function parameters that best allow the associated fuzzy inference system to track the input/output data. The literature indicates that the self-predicting model of ANFIS is better than other models in a lot of fields. Shortcomings and advantages of both approaches are here highlighted.


Adaptive Neuro-Fuzzy Inference System Stochastic Models Longevity Projections 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Valeria D’Amato
    • 1
  • Gabriella Piscopo
    • 2
  • Maria Russolillo
    • 1
  1. 1.Department of Economics and StatisticsUniversity of SalernoSalernoItaly
  2. 2.Department of EconomicsUniversity of GenoaGenoaItaly

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