Pattern recognition using fuzzy inference with lacked input data

In the pattern processing, it is difficult that all features are extracted correctly. For the system used the fuzzy inference, if input datum is lacked, the system does not work well. In this paper, a frame work of a modified fuzzy inference method with lacked input data is introduced. And experimental results are provided. In the proposed method that is modified from the Mamdani's fuzzy inference, a result of each rule is the adjustment for the purpose of protection from influence of lacked input data. The adjustment of result fuzzy labels at each rule is used the degree of importance which is set up in the rules by human. And in the results of simply experiment, the system can infer well with lacked input data used this method. In results of simple experiments used a set of five fuzzy rules (three input and three output), when one or two input data are lacked, the system infers correctly.