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.

本文提出了一种基于特定数据融合方法的最小参数系统（MPS）模型，能够减少不确定性并提供更准确的结果。我们已将该模型应用于从电子鼻获取的数据。该模型在结构上具有创新性，采用典型的有序加权平均（OWA）算子作为数据融合方法，命名为基于OWA的MPS模型。不同的 OWA 算子已经过测试，以确定用于聚合从每个传感器获取的数据的权重。根据金属氧化物半导体传感器的结构，响应速率是温度和湿度的函数。因此，数据是在不同的温度和湿度条件下获得的，以确保所提出的方案在实际应用中的适用性。