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A unified neurofuzzy model for classification

Gao, M., Hong, X. and Harris, C. J. (2014) A unified neurofuzzy model for classification. International Journal of Systems Science, 45 (10). pp. 2158-2171. ISSN 0020-7721

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To link to this item DOI: 10.1080/00207721.2013.763301


This work proposes a unified neurofuzzy modelling scheme. To begin with, the initial fuzzy base construction method is based on fuzzy clustering utilising a Gaussian mixture model (GMM) combined with the analysis of covariance (ANOVA) decomposition in order to obtain more compact univariate and bivariate membership functions over the subspaces of the input features. The mean and covariance of the Gaussian membership functions are found by the expectation maximisation (EM) algorithm with the merit of revealing the underlying density distribution of system inputs. The resultant set of membership functions forms the basis of the generalised fuzzy model (GFM) inference engine. The model structure and parameters of this neurofuzzy model are identified via the supervised subspace orthogonal least square (OLS) learning. Finally, instead of providing deterministic class label as model output by convention, a logistic regression model is applied to present the classifier’s output, in which the sigmoid type of logistic transfer function scales the outputs of the neurofuzzy model to the class probability. Experimental validation results are presented to demonstrate the effectiveness of the proposed neurofuzzy modelling scheme.

Item Type:Article
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:32277
Uncontrolled Keywords:neurofuzzy modelling; Gaussian mixture models; subspace membership functions; rule reduction
Publisher:Taylor & Francis

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