Medical diagnosis applications using a novel interactively recurrent self-evolving fuzzy CMAC model

Jyun Guo Wang, Shen Chuan Tai, Cheng Jian Lin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4092-4098
Number of pages7
ISBN (Electronic)9781479914845
DOIs
Publication statusPublished - 2014 Sep 3
Event2014 International Joint Conference on Neural Networks, IJCNN 2014 - Beijing, China
Duration: 2014 Jul 62014 Jul 11

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Other

Other2014 International Joint Conference on Neural Networks, IJCNN 2014
Country/TerritoryChina
CityBeijing
Period14-07-0614-07-11

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

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