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Neurofuzzy network model construction using Bézier-Bernstein polynomial functions

Harris, C.J. and Hong, X. ORCID: (2000) Neurofuzzy network model construction using Bézier-Bernstein polynomial functions. IEE Proceedings-Control Theory and Applications, 147 (3). pp. 337-343. ISSN 1350-2379

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To link to this item DOI: 10.1049/ip-cta:20000394


Neurofuzzy modelling systems combine fuzzy logic with quantitative artificial neural networks via a concept of fuzzification by using a fuzzy membership function usually based on B-splines and algebraic operators for inference, etc. The paper introduces a neurofuzzy model construction algorithm using Bezier-Bernstein polynomial functions as basis functions. The new network maintains most of the properties of the B-spline expansion based neurofuzzy system, such as the non-negativity of the basis functions, and unity of support but with the additional advantages of structural parsimony and Delaunay input space partitioning, avoiding the inherent computational problems of lattice networks. This new modelling network is based on the idea that an input vector can be mapped into barycentric co-ordinates with respect to a set of predetermined knots as vertices of a polygon (a set of tiled Delaunay triangles) over the input space. The network is expressed as the Bezier-Bernstein polynomial function of barycentric co-ordinates of the input vector. An inverse de Casteljau procedure using backpropagation is developed to obtain the input vector's barycentric co-ordinates that form the basis functions. Extension of the Bezier-Bernstein neurofuzzy algorithm to n-dimensional inputs is discussed followed by numerical examples to demonstrate the effectiveness of this new data based modelling approach.

Item Type:Article
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:18504
Uncontrolled Keywords:Bezier-Bernstein polynomial functions, Delaunay input space partitioning, barycentric co-ordinates, basis functions, fuzzification, fuzzy membership function, inverse de Casteljau procedure, neurofuzzy network model construction, quantitative artificial neural networks, structural parsimony

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