REVIEW ARTICLE
A New ANFIS Model based on Multi-Input Hamacher T-norm and Subtract Clustering
Feng-Yi Zhang*, Zhi-Gao Liao
Department of Management, Guangxi University of Science and Technology, Liuzhou, Guangxi, 545000, China
Article Information
Identifiers and Pagination:
Year: 2014Volume: 8
First Page: 833
Last Page: 838
Publisher Id: TOMEJ-8-833
DOI: 10.2174/1874155X01408010833
Article History:
Received Date: 08/01/2015Revision Received Date: 15/01/2015
Acceptance Date: 16/01/2015
Electronic publication date: 31/12/2014
Collection year: 2014
© 2014 Zhang and Liao
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
This paper proposed a novel adaptive neuro-fuzzy inference system (ANFIS), which combines subtract clustering, employs adaptive Hamacher T-norm and improves the prediction ability of ANFIS. The expression of multiinput Hamacher T-norm and its relative feather has been originally given, which supports the operation of the proposed system. Empirical study has testified that the proposed model overweighs early work in the aspect of benchmark Box- Jenkins dataset and may provide a practical way to measure the importance of each rule.
Keywords: ANFIS, Hamacher T-norm, Subtract Clustering, T-norm.
