Genetic algorithm design of neural network wavefront predictors

Peter J. Gallant; George J. M. Aitken

doi:10.1117/12.462625

20 March 2003 Genetic algorithm design of neural network wavefront predictors

Peter J. Gallant, George J. M. Aitken

Proceedings Volume 4884, Optics in Atmospheric Propagation and Adaptive Systems V; (2003) https://doi.org/10.1117/12.462625
Event: International Symposium on Remote Sensing, 2002, Crete, Greece

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.

Citation Download Citation

Peter J. Gallant and 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); https://doi.org/10.1117/12.462625

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