In order to modify numerical values into linguistic values which fit human sense, an intelligent interface using fuzzy associative inference is proposed. The proposed interface uses associative memory networks and is constructed by a method which makes it easy to represent and to modify fuzzy rules. The associative memory networks use an association matrix
Me (which is a normalized matrix constructed from a correlation matrix
M, a bias matrix
B, and a scale parameter a) in order to easily carry out refinement and cut-and-paste operations on fuzzy rules, a difficult procedure for conventional systems. For the refinement of fuzzy membership functions, we propose “category based learning”, which carries out separated modifying on a fuzzy label category. Using the intelligent interface, we compose a command spelling corrector based on a multi-layer fuzzy rule set and uses an association matrix
Me to carry out associative inference. The spelling corrector application shows, (1) the usefulness of associative inference for a multi-layer fuzzy rule set using simultaneous bottom-up and top-down processing and, (2) the usefulness of category based learning for deriving fuzzy membership functions, which show human error feature.
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