Please use this identifier to cite or link to this item:
|Title:||The min-max function differentiation and training of fuzzy neural networks|
|Citation:||Zhang, X., Hang, C.-C., Tan, S., Wang, P.-Z. (1996). The min-max function differentiation and training of fuzzy neural networks. IEEE Transactions on Neural Networks 7 (5) : 1139-1150. ScholarBank@NUS Repository. https://doi.org/10.1109/72.536310|
|Abstract:||This paper discusses the Δ-rule and training of min-max neural networks by developing a differentiation theory for min-max functions, the functions containing min (∧) and/or max (∨) operations. We first prove that under certain conditions all min-max functions are continuously differentiable almost everywhere in the real number field R-fraktur sign and derive the explicit formulas for the differentiation. These results are the basis for developing the Δ-rule for the training of min-max neural networks. The convergence of the new Δ-rule is proved theoretically using the stochastic theory, and is demonstrated with a simulation example. © 1996 IEEE.|
|Source Title:||IEEE Transactions on Neural Networks|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on May 27, 2018
WEB OF SCIENCETM
checked on Apr 10, 2018
checked on May 18, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.