Applications of Artificial Neutral Networks in Mushroom Edibility Classification

Authors

  • Gustavo Munoz Faculty Advisor: Sungmoon Jung Department of Civil Engineering
  • Sungmoon Jung Florida State University

Keywords:

artificial neural networks, synthesis, neurology

Abstract

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 combinations of number of hidden nodes, separation of training, validation, and test data and number of iterations. Qualitative identification of an optimal combination of network parameters will provide a basis toward applications of artificial neural networks in future civil engineering endeavors.

Author Biography

Gustavo Munoz, Faculty Advisor: Sungmoon Jung Department of Civil Engineering

Gustavo Munoz is an undergraduate student studying civil engineering at the FAMU–FSU College of Engineering. He is also a research assistant at the Laboratory for Intelligent Materials and Structures at the FAMU-FSU COE. His research interests are centered on Structural Control and Smart Structures.

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Published

2011-03-01

Issue

Vol. 1 No. 1 (2011)

Section

Research Articles

License

All works published in The Owl are published under a Creative Commons Attribution, Non-Commercial, Share-Alike (CC-BY-NC-SA) license. The author retains copyright.