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1998

Evolutionary Optimized Tensor Product Bernstein Polynomials versus Backpropagation Networks

11 years 8 months ago
Evolutionary Optimized Tensor Product Bernstein Polynomials versus Backpropagation Networks
In this paper a new approach for approximation problems involving only few input and output parameters is presented and compared to traditional Backpropagation Neural Networks (BPNs). The basic model is a Tensor Product Bernstein Polynomial (TPBP) for which suitable control points need to be found. It is shown that a TPBP can also be interpreted as a special class of feed-forward neural networks where control point coordinates are represented by input weights. Although optimal control points for a TPBP leading to the smallest possible approximation errors can be determined by the Method of Least Squares (MLS), this approach has only poor generalization capabilities. Instead, the usage of a (
Günther R. Raidl, Gabriele Kodydek
Added 01 Nov 2010
Updated 01 Nov 2010
Type Conference
Year 1998
Where NC
Authors Günther R. Raidl, Gabriele Kodydek
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