20 March 2003 Genetic algorithm design of neural network wavefront predictors
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Abstract
A genetic algorithm (GA) is employed to determine the structure of measured, Shack-Hartmann data and its optimum artificial neural-network (ANN) predictor. In the GA approach there are no preordained architectures imposed. The NN architecture that evolves out of many generations of adaptation can also be interpreted as a mapping of the signal complexity. The GA approach inherently addresses the problems of generalization, over fitting of data, and the trade-off between ANN complexity and performance. One objective was to establish how much improvement could ideally be expected from NNs compared to linear techniques. The principal conclusions are: (i) The main input-output relationship is linear with only a small contribution from the nonlinear elements. (ii) The improvement achievable with ANNs compared to optimal linear predictors was less than a 10% reduction in predictor error. (iii) The optimum temporal input window of tip-tilt data corresponds to the time constant introduced by aperture averaging.
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Peter J. Gallant, George J. M. Aitken, "Genetic algorithm design of neural network wavefront predictors", Proc. SPIE 4884, Optics in Atmospheric Propagation and Adaptive Systems V, (20 March 2003); doi: 10.1117/12.462625; https://doi.org/10.1117/12.462625
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