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.
This work was supported by grants from the National Natural Sciences Foundation of China (No.60375023 and No. 60574059) and the National Basic Research Program of China (No. 2005CB321800).
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Liu, P., Luo, Q., Yang, W., Yi, D. (2006). The Learning Algorithm for a Novel Fuzzy Neural Network. In: Huang, DS., Li, K., Irwin, G.W. (eds) Intelligent Computing. ICIC 2006. Lecture Notes in Computer Science, vol 4113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816157_24
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DOI: https://doi.org/10.1007/11816157_24
Publisher Name: Springer, Berlin, Heidelberg
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