Applications of Artificial Neutral Networks in Mushroom Edibility Classification

Applications of Artificial Neutral Networks in Mushroom Edibility Classification

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We report the accuracy of a two-layer, back-propagation artificial neural network in identifying edibility of a set of random mushrooms.Mushrooms edibility was synthesized using many different characteristics.Tests were run using different focusrite rednet r1 combinations of number of hidden nodes, separation of training, validation, and test data and number of iterations.Qualitative identification of an opi the color that keeps on giving optimal combination of network parameters will provide a basis toward applications of artificial neural networks in future civil engineering endeavors.