Abstract
This papers proposes two novel approaches for the identification of Takagi-Sugeno fuzzy models with time variant and invariant features. The proposed Mixed Fuzzy Clustering algorithm is proposed for determining the parameters of Takagi-Sugeno fuzzy models in two different ways: (1) the antecedent fuzzy sets are determined based on the partition matrix generated by the Mixed Fuzzy Clustering algorithm; (2) the input features are transformed using the same algorithm and the antecedent fuzzy sets are derived using Fuzzy C-Means clustering. The proposed approaches are tested on four different health care applications: readmissions in intensive care units, administration of vasopressors and mortality. The results show that the proposed clustering algorithm resulted in an increase of the performance of the fuzzy models in three out of four applications in comparison to the use of Fuzzy C-Means.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
