Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/96048
Title: Supervised pseudo self-evolving cerebellar algorithm for generating fuzzy membership functions
Authors: Ang, K. K.
Quek, Chai
Keywords: DRNTU::Engineering::Computer science and engineering
Issue Date: 2011
Series/Report no.: Expert systems with applications
Abstract: The proper generation of fuzzy membership function is of fundamental importance in fuzzy applications. The effectiveness of the membership functions in pattern classifications can be objectively measured in terms of interpretability and classification accuracy in the conformity of the decision boundaries to the inherent probabilistic decision boundaries of the training data. This paper presents the Supervised Pseudo Self-Evolving Cerebellar (SPSEC) algorithm that is bio-inspired from the two-stage development process of the human nervous system whereby the basic architecture are first laid out without any activity-dependent processes and then refined in activity-dependent ways. SPSEC first constructs a cerebellar-like structure in which neurons with high trophic factors evolves to form membership functions that relate intimately to the probability distributions of the data and concomitantly reconcile with defined semantic properties of linguistic variables. The experimental result of using SPSEC to generate fuzzy membership functions is reported and compared with a selection of algorithms using a publicly available UCI Sonar dataset to illustrate its effectiveness.
URI: https://hdl.handle.net/10356/96048
http://hdl.handle.net/10220/11135
DOI: http://dx.doi.org/10.1016/j.eswa.2011.08.001
Rights: © 2011 Elsevier Ltd.
metadata.item.grantfulltext: none
metadata.item.fulltext: No Fulltext
Appears in Collections:SCSE Journal Articles

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