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Using fuzzy partitions to create fuzzy systems from input-output data and set the initial weights in a fuzzy neural network

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3 Author(s)
Yinghua Lin ; Center for Nonlinear Studies, Los Alamos Nat. Lab., NM, USA ; G. A. Cunningham ; S. V. Coggeshall

We create a set of fuzzy rules to model a system from input-output data by dividing the input space into a set of subspaces using fuzzy partitions. We create a fuzzy rule for each subspace as the input space is being divided. These rules are combined to produce a fuzzy rule based model from the input-output data. If more accuracy is required, we use the fuzzy rule-based model to determine the structure and set the initial weights in a fuzzy neural network. This network typically trains in a few hundred iterations. Our method is simple, easy, and reliable and it has worked well when modeling large “real world” systems

Published in:

IEEE Transactions on Fuzzy Systems  (Volume:5 ,  Issue: 4 )