Regression using fuzzy adaptive resonant theory neural network

A new neural network paradigm based on fuzzy adaptive resonant theory, which yields quantitative results, is presented. The performance of this network in building calibration models for quantitative gas analysis is estimated and compared with other well-established methods. A faster training phase and higher accuracy in regression characterise the new paradigm