Abstract
Symmetric polygonal fuzzy numbers are employed to construct a class of novel feedforward fuzzy neural networks (FNN's)-the polygonal FNN's. Their input-output (I/O) relationships are built upon a novel fuzzy arithmetic and extension principle for the polygonal fuzzy numbers. We build the topological architecture of a three layer polygonal FNN, and present its I/O relationship representation. Also the fuzzy BP learning algorithm for the polygonal fuzzy number connection weights and thresholds is developed based on calculus of max-min (∨ -∧) functions. At last some simulation examples are compared to show that our model possess strong I/O ability and generalization capability. © Springer-Verlag Berlin Heidelberg 2006.
Cite
CITATION STYLE
Liu, P., Luo, Q., Yang, W., & Yi, D. (2006). The learning algorithm for a novel fuzzy neural network. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4113 LNCS-I, pp. 247–258). Springer Verlag. https://doi.org/10.1007/11816157_24
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