| Title |
Structural design of Optimized Interval Type-2 FCM Based RBFNN : Focused on Modeling and Pattern Classifier |
| Authors |
김은후(Kim, Eun-Hu) ; 송찬석(Song, Chan-Seok) ; 오성권(Oh, Sung-Kwun) ; 김현기(Kim, Hyun-Ki) |
| DOI |
https://doi.org/10.5370/KIEE.2017.66.4.692 |
| Keywords |
Interval type-2 FCM clustering ; principal component algorithm ; Linear discriminant analysis ; Least square estimation ; Weighted least square estimation ; Differential evolution |
| Abstract |
In this paper, we propose the structural design of Interval Type-2 FCM based RBFNN. Proposed model consists of three modules such as condition, conclusion and inference parts. In the condition part, Interval Type-2 FCM clustering which is extended from FCM clustering is used. In the conclusion part, the parameter coefficients of the consequence part are estimated through LSE(Least Square Estimation) and WLSE(Weighted Least Square Estimation). In the inference part, final model outputs are acquired by fuzzy inference method from linear combination of both polynomial and activation level obtained through Interval Type-2 FCM and acquired activation level through Interval Type-2 FCM. Additionally, The several parameters for the proposed model are identified by using differential evolution. Final model outputs obtained through benchmark data are shown and also compared with other already studied models' performance. The proposed algorithm is performed by using Iris and Vehicle data for pattern classification. For the validation of regression problem modeling performance, modeling experiments are carried out by using MPG and Boston Housing data. |