TY - GEN
T1 - Medical diagnosis applications using a novel interactively recurrent self-evolving fuzzy CMAC model
AU - Wang, Jyun Guo
AU - Tai, Shen Chuan
AU - Lin, Cheng Jian
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/9/3
Y1 - 2014/9/3
N2 - In this paper, a recurrent self-evolving Fuzzy Cerebellar Model Articulation Controller (FCMAC) model for classification problems is developed, namely the interactively recurrent self-evolving fuzzy Cerebellar Model Articulation Controller (IRSFCMAC). The interactively recurrent structure in an IRSFCMAC is formed as external loops and internal feedbacks by feeding the rule firing strength to itself and others rules. The IRSFCMAC learning starts with an empty rule base and all of rules are generated and learned online, through a simultaneous structure and parameter learning, while the relative parameters are learned through a gradient descent algorithm. The proposed IRSFCMAC is tested by the four benchmarked classification problems and compared with the well-known traditional FCMAC. Experimental results show that the proposed IRSFCMAC model enhanced classification performance results, in terms of accuracy and RMSE.
AB - In this paper, a recurrent self-evolving Fuzzy Cerebellar Model Articulation Controller (FCMAC) model for classification problems is developed, namely the interactively recurrent self-evolving fuzzy Cerebellar Model Articulation Controller (IRSFCMAC). The interactively recurrent structure in an IRSFCMAC is formed as external loops and internal feedbacks by feeding the rule firing strength to itself and others rules. The IRSFCMAC learning starts with an empty rule base and all of rules are generated and learned online, through a simultaneous structure and parameter learning, while the relative parameters are learned through a gradient descent algorithm. The proposed IRSFCMAC is tested by the four benchmarked classification problems and compared with the well-known traditional FCMAC. Experimental results show that the proposed IRSFCMAC model enhanced classification performance results, in terms of accuracy and RMSE.
UR - http://www.scopus.com/inward/record.url?scp=84908466439&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84908466439&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2014.6889511
DO - 10.1109/IJCNN.2014.6889511
M3 - Conference contribution
AN - SCOPUS:84908466439
T3 - Proceedings of the International Joint Conference on Neural Networks
SP - 4092
EP - 4098
BT - Proceedings of the International Joint Conference on Neural Networks
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 International Joint Conference on Neural Networks, IJCNN 2014
Y2 - 6 July 2014 through 11 July 2014
ER -