Neuro-fuzzy systems derived from quasi-triangular norms
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Most neuro-fuzzy systems proposed in the past decade employ "engineering implications" defined by a t-norm, e.g. the minimum or the product. We apply a new class of operators called quasi-triangular norms for the construction of neuro-fuzzy systems. These operators depend on a certain parameter /spl nu/ and change their functional forms between a t-norm and a t-conorm. Consequently, the structure of neuro-fuzzy systems presented in the paper is determined in the process of learning. Learning procedures are derived and simulation examples are presented.
[1] Detlef Nauck,et al. Foundations Of Neuro-Fuzzy Systems , 1997 .
[2] Jacek M. Leski,et al. Fuzzy and Neuro-Fuzzy Intelligent Systems , 2000, Studies in Fuzziness and Soft Computing.
[3] Leszek Rutkowski,et al. A general approach to neuro-fuzzy systems , 2001, 10th IEEE International Conference on Fuzzy Systems. (Cat. No.01CH37297).
[4] Marian B. Gorzalczany. Computational Intelligence Systems and Applications - Neuro-Fuzzy and Fuzzy Neural Synergisms , 2002, Studies in Fuzziness and Soft Computing.