Artificial Neural Networks Equivalent to Fuzzy Algebra T-Norm Conjunction Operators
Lazaros S. Iliadis, Stefanos Spartalis, Theodore E. Simos, George B. Maroulis · AIP conference proceedings · 2007
This paper describes the construction of three Artificial Neural Networks with fuzzy input and output, imitating the performance of fuzzy algebra conjunction operators. More specifically, it is applied over the results of a previous research effort that used T‐Norms in order to produce a characteristic torrential risk index that unified the partial risk indices for the area of Xanthi. Each one of the three networks substitutes a T‐Norm and consequently they can be used as equivalent operators. This means that ANN performing Fuzzy Algebra operations can be designed and developed.