Artificial Neural Networks Applied to Odor Classification for Chemical Compounds

Abstract Three-layer artificial neural networks (ANN) with back-propagation of error were used to classify the odors of chemical compounds. The network's architecture and parameters were optimized, and an empirical rule for dynamically adjusting the learning rate of network was put forward. It was found that the network gave the maximum recognition rate of 100% and converged quickly by using the plural semiconductor gas sensors' response data in combination with the molecular structure codes of odorants. The network's predictive ability for untrained samples was also satisfactory.