บทคัดย่องานวิจัย

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Predicting aflatoxin contamination in peanuts: A genetic algorithm/neural network approach.

HENDERSON, C.E., POTTER, W.D., MCCLENDON, R.W. and HOOGENBOOM, G.

Applied Intelligence 12: 183-192.

2000

บทคัดย่อ

Predicting aflatoxin contamination in peanuts: A genetic algorithm/neural network approach.

The development of a genetic algorithm/backpropagation neural network hybrid (GA/BPN) is described in which a genetic algorithm is used to find architectures and backpropagation parameter values simultaneously for a backpropagation neural network that predicts aflatoxin contamination levels in peanuts based on environmental data. Learning rate, momentum and number of hidden nodes are the parameters that are set by the genetic algorithm. A three-layer feed-forward network with logistic activation functions is used. Inputs to the network are soil temperature, drought duration, crop age, and accumulated heat units. The project shoed that the GA/BPN approach automatically finds highly fit parameter sets for backpropagation nerual networks for the aflatoxin problem.