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The equivalence between fuzzy logic systems and feedforward neural networks

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2 Author(s)
Hong-Xing Li ; Dept. of Math., Beijing Normal Univ., China ; Chen, C.L.P.

Demonstrates that fuzzy logic systems and feedforward neural networks are equivalent in essence. First, we introduce the concept of interpolation representations of fuzzy logic systems and several important conclusions. We then define mathematical models for rectangular wave neural networks and nonlinear neural networks. With this definition, we prove that nonlinear neural networks can be represented by rectangular wave neural networks. Based on this result, we prove the equivalence between fuzzy logic systems and feedforward neural networks. This result provides us a very useful guideline when we perform theoretical research and applications on fuzzy logic systems, neural networks, or neuro-fuzzy systems

Published in:

Neural Networks, IEEE Transactions on  (Volume:11 ,  Issue: 2 )

Date of Publication:

Mar 2000

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