Hitachi, Ltd.

Toshihide Ichimori

Akira Maeda

Motohisa Funabashi

Mikio Yoda

Toshihiko Nakano

Hiromasa Yamaoka

Chiaki Hirai

Hitoshi Ashida

Nobuyuki Fujikura

Mitsuo Yanagi

Yori Takahashi

A rule generation apparatus includes a label presenter in which when producing from training data including a set of specific values related to input and output variables rules representing input/output relationships between the input and output variables, numeric data of the training data is converted into categorical data expressed by symbols to generate an instance table, an RI device for extracting rules from the instance table, and a rule converter for converting the extracted rules into fuzzy rules. When the training data is divided to be distributively stored in a plurality of server processors, label assignment is conducted by each server processor such that a client processor later combines instance tables with each other to achieve rule induction and conversion.

Rule generation system and method of generating rule

An information processing apparatus is capable of achieving high-level functions such as high-speed processing and learning without requiring a complex algorithm based on, for example, knowledge processing. A plurality of sets of known input data and correct output data corresponding thereto are beforehand stored. The stored input data is called response pattern data. Data actually inputted in the system operation is supplied to the plural sets of data. For each set of data, the input data is compared with the response pattern data to evaluate a distance (degree of similarity) therebetween. Results of evaluations for the respective sets of data are combined with the associated output data, for example, according to weighted average values on the basis of the evaluated distance, thereby generating final outputs.

Information processing apparatus and monitoring apparatus

A fuzzy rule acquisition method for a fuzzy inference system in which a fuzzy inference is achieved based on fuzzy knowledge including fuzzy rules each described in a format having an IF part and a THEN part, and membership functions defining meanings of respective propositions described in the IF part and THEN part, and in which a fuzzy inference is performed for at least one input value by using the fuzzy rules of the fuzzy knowledge to obtain a result of the execution of the fuzzy inference, the result of the execution of the fuzzy inference is compared with a teaching value to obtain an inference error, the errors of the fuzzy rules are obtained by using the inference error, it is judged whether each of the fuzzy rules is contradictory, based on the errors of corresponding rules of the fuzzy rules, and a fuzzy rule judged as contradictory is modified to dissolve the contradiction.

Fuzzy rule acquisition method and apparatus for fuzzy inference system

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Kawasaki, Japan

Toshihide Ichimori