Fuzzy artificial network and its application to a command spelling corrector

The authors propose a fuzzy artificial network (FAN) which utilizes associative memories and is constructed by a method which makes it easy to represent and to modify fuzzy rule sets. Whereas conventional fuzzy inference methods induce much fuzziness on multilayered fuzzy rule sets, the associative-memory-based FAN results in inferences which fit human senses better. This type of fuzzy inference is called associative inference. For memorizing fuzzy rule sets, the FAN system employs a correlation matrix which is constructed from a nominal correlation matrix, a bias matrix, and a scale parameter, so that it is easy to carry out refinement and cut-and-paste operations for rule sets. Using a FAN development system, a command spelling corrector is proposed which uses a multilayered fuzzy rule set. The spelling corrector application shows the eligibility of associative inference for multilayered fuzzy rule sets