K. Nakaoka

A Proposal on Payoffs and Apportionment of Credits of Fuzzy Classifier System : Finding of Knowledge for Large Scale Systems

Takeshi Furuhashi, Yoshiki Uchikawa, Hiroshi Maeda, 1996 .

A Proposal of Genetic Algorithm with a Local Improvement Mechanism and Finding of Fuzzy Rules

Takeshi Furuhashi, Yoshiki Uchikawa, Takeshi Furuhashi, 1995 .

Suppression of excessive fuzziness using multiple fuzzy classifier systems

Takeshi Furuhashi, Yoshiki Uchikawa, Ken Nakaoka, 1994, Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference.

A New Approach to Genetic Based Machine Learning and an Efficient Finding of Fuzzy Rules - Proposal of Nagoya Approach -

Y. Uchikawa, Y. Miyata, K. Nakaoka, 1994, IEEE/Nagoya-University World Wisepersons Workshop.

A study on apportionment of credits of fuzzy classifier system for knowledge acquisition of large scale systems

Yoshiki Uchikawa, Ken Nakaoka, T. Furuhashi, 1994, Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference.

A Study on Knowledge Finding Using Fuzzy Classifier System

Takeshi Furuhashi, Yoshiki Uchikawa, Hiroshi Maeda, 1995 .

An Acquuitsition of Control Knowledge Using Multiple Fuzzy Classifier Systems

Takeshi Furuhashi, Yoshiki Uchikawa, Koji Morikawa, 1994 .

An efficient finding of fuzzy rules using a new approach to genetic based machine learning

Yoshiki Uchikawa, Takeshi Furuhashi, Ken Nakaoka, 1995, Proceedings of 1995 IEEE International Conference on Fuzzy Systems..

A new approach to genetic based machine learning for efficient improvement of local portions of chromosomes

T. Furuhashi, Y. Miyata, K. Nakaoka, 1994, ETFA '94. 1994 IEEE Symposium on Emerging Technologies and Factory Automation. (SEIKEN) Symposium) -Novel Disciplines for the Next Century- Proceedings.