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In palynology, neural networks have been successfully applied for the classification of pollen grains. For this purpose, RPROP was selected as training algorithm of a neural network for the classification of a dataset previously reported. The RPROP variants with and without weight backtracking were considered. A genetic algorithm was used to optimize the ANN topology (number of hidden layers and number of neurons per layer), training and test ratios, initial weights and number of iterations. The topologies found and reported in this document shows better classification results than those previously reported. Although it is not the final goal, this development is intended to be useful in identifying the pollen types in honey produced in the Misantla region.

Configuración de red neuronal para el análisis de polen