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The radial basis function network (RBFN) is a multi-layer feed-forward neural network consisting of simple processing elements (neurons) with weighted interconnections between the elements. The weighted interconnections between the neurons can be calculated by any one of a wide range of optimisation algorithms. We will demonstrate that although the gradient descent method produces a "better" solution to the sample problem, limiting the number of allowable interconnection weights enhances the tolerance of the network to errors in the DOE reconstruction. In addition, the genetic algorithm optimisation method is significantly faster than the gradient descent method for the same number of allowable weight values.
Date of Conference: 12-17 June 2005