A Generalized Functional Network for a Classifier-Quantifiers Scheme in a Gas-Sensing System

This paper discusses a new computational scheme based on Functional Networks and applies it to the problem of classification and quantification of gas species in a mixture. A Generalized Functional Network as a new classifier is proposed in order to improve the potentialities of the standard Functional Network classifier. Both methodology and learning algorithm are derived. The performance of this new classifier is examined by using experimental applications. A comparative study with the most common classification algorithms is carried out, by showing the high-quality performance of the proposed classifier. The classifier interacts with some quantifiers, based on Functional Networks again and Finite Differences. The scheme of the quantifiers was previously proposed for single gas exposure applications and is here extended to the multi-gas case. Numerical results show that our approach behaves quite satisfactorily.