When we attempt to model a complex system including human as an important component, it may be difficult to represent the system by a deterministic mathematical model. The main reason of this difficulty is that the system itself inherently has some fuzziness concerning subjective judgement of human. In this paper, we propose a fuzzy nonlinear regression method with RFLN (RCE-based Fuzzy Learning Network), which is capable of extracting knowledge of the experts automatically. RFLN is an extended RCE (Restricted Coulomb Energy) model, hence it needs few iterations in learning and its additional learning is easy. The proposed method has higher flexibility than fuzzy linear regression models. We propose learning algorithms to identify a nonlinear interval model which approximately includes all the given input-output data. The proposed method has characteristics of faster learning and of easier additional learning. The effectiveness of the method is shown by numerical experiments.
|出版ステータス||Published - 1997 1 1|
|イベント||Proceedings of the 1997 6th IEEE International Conference on Fussy Systems, FUZZ-IEEE'97. Part 1 (of 3) - Barcelona, Spain|
継続期間: 1997 7 1 → 1997 7 5
|Other||Proceedings of the 1997 6th IEEE International Conference on Fussy Systems, FUZZ-IEEE'97. Part 1 (of 3)|
|Period||97/7/1 → 97/7/5|
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