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Parametric bivariate surfaces with neural networks

Mingo L. F., Martinez A., Gimenez V., Castellanos J.

Machine Graphics and Vision

2000

Vol. 9, No. 1/2

41-46

This paper presents the application of Enhaced Neural Networks (ENN) to the field of Image Processing, more precisely, to the field of surfaces approximation via the generalization property that ENNs have. This architecture can perform a polynominal approximation of a given pattern sen in such a way that if the net has "n" hidden layers, then it will compute the "n"+2 degree polynominal approximation to its pattern set. Moreover, the behaviour of this net can be modified just modifying the activation functon f(x) of some neurons, in such a way that the net will compute the approximation to the pattern set using a function basis of functions f(x), this way the net computes the non lineal combination of basis elements to output the desired approximation. ENNs are used to represented a surface approximation. Some examples, concerning results when learning surfaces, are explained along this paper. Results are good since the Mean squared Error is very low and the computation time too.