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The min-max function differentiation and training of fuzzy neural networks

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4 Author(s)
Xinghu Zhang ; Dept. of Electr. Eng., Nat. Univ. of Singapore, Singapore ; Chang-Chieh Hang ; Shaohua Tan ; Pei-Zhuang Wang

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 (V) operations. We first prove that under certain conditions all min-max functions are continuously differentiable almost everywhere in the real number field ℜ 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

Published in:

Neural Networks, IEEE Transactions on  (Volume:7 ,  Issue: 5 )

Date of Publication:

Sep 1996

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