Building fuzzy graphs from examples

Function approximation based on example data has gained considerable interest in the past. The automatic extraction of a fuzzy rule base has proven to be a powerful tool to build approximators that allow an interpretation of the underlying model. In contrast to most known systems which find a set of...

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Bibliographic Details
Main Authors: Berthold, M.R., Huber, K.-P.
Format: Conference Proceeding
Language:English
Subjects:
Artificial intelligence
Bismuth
Fuzzy logic
Fuzzy sets
Input variables
Interpolation
Neural networks
Partitioning algorithms
Radial basis function networks
Training data
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Summary:Function approximation based on example data has gained considerable interest in the past. The automatic extraction of a fuzzy rule base has proven to be a powerful tool to build approximators that allow an interpretation of the underlying model. In contrast to most known systems which find a set of rules based on a global grid that covers the whole input space, a different approach is presented in this paper. A constructive algorithm finds a set of local individual rules forming a fuzzy graph. The proposed algorithm builds the fuzzy graph from scratch, without the need to control additional parameters and shows promising performance and robustness against noise on an artificial dataset.
DOI:10.1109/FUZZY.1996.551809