An Electronic Nose Using Neural Networks with Effective Training Data Selection

Abstract

An electronic nose developed from metal oxide gas sensors is applied to test smoke of three general household burning materials under different environments. Generally training data is randomly selected for a layered neural network with error back-propagation (BP). Randomized training data always contain redundant data that lengthen training time without improving classification performance. This paper proposes an effective method to select training data based on a similarity index (SI). The SI ensures that only the most valuable training data is included in the training data set. The proposed method is applied to remove redundant data from the training data set before being fed to the layered neural network based on BP. Results verified high classification performance by using a small number of training data from proposed method.