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|Title:||The min-max function differentiation and training of fuzzy neural networks||Authors:||Zhang, X.
|Issue Date:||1996||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.||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||URI:||http://scholarbank.nus.edu.sg/handle/10635/81253||ISSN:||10459227|
|Appears in Collections:||Staff Publications|
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