LAUSR

Abstract This paper proposes a minimum parameter system (MPS) model based on a particular data fusion method capable of reducing the uncertainties and providing more accurate results. We have applied the model to the data taken from an electronic nose. The model is innovative in the structure and uses the typical ordered weighted averaging (OWA) operators as a data fusion method, named OWA-based MPS model. Different OWA operators have been tested to determine the weights for aggregating the data acquired from each sensor. According to the structure of the metal–oxide–semiconductor sensor, the response rate is a function of temperature and humidity. Hence, the data is obtained in different temperature and humidity conditions to ensure the appropriateness of the proposed scheme for real applications. Comparing the results with the existing method shows the cost efficiency, lower computational complexity, and fast response rate of the proposed model.