• Corpus ID: 13507562

Enhancing the Performance of Neurofuzzy Predictors by Emotional Learning Algorith

@article{Lucas2003EnhancingTP,
  title={Enhancing the Performance of Neurofuzzy Predictors by Emotional Learning Algorith},
  author={Caro Lucas and Ali Abbaspour and Ali Gholipour and Babak Nadjar Araabi and Mehrdad Fatourechi},
  journal={Informatica (Slovenia)},
  year={2003},
  volume={27},
  pages={165-174}
}
Neural networks and Neurofuzzy models have been successfully used in the prediction of nonlinear time series. Several learning methods have been introduced to train the Neurofuzzy predictors, such as ANFIS, ASMOD and FUREGA. Many of these methods, constructed over Takagi Sugeno fuzzy inference system, are characterized by high generalization. However, they differ in computational complexity. The emotional Learning, which is successfully used in bounded rational decision making, is introduced as… 
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