基于非参数模型的气体浓度的逆向预测

气体传感器阵列是一种重要且强大的检测气体和测量浓度的技术。传统的描述传感器响应与气体浓度之间关系的策略是使用一些特定的非线性参数模型。本文使用非参数模型描述传感器响应随气体浓度的变化，有效避免了模型的错误假定。提出一种基于非参数模型逆向预测气体浓度的方法。还提出通过数据驱动选择可调参数的方法。数值模拟结果表明，当传感器阵列的实际模型未知或模型假定错误时，非线性参数模型的性能劣于非参数模型，实际数据分析也验证了这一点。

Inverse prediction of gas concentration based on nonparametric model

Gas sensor array is an important and powerful technique for detecting gas and measuring gas concentrations. The conventional strategy to describe the relationship between the response of the sensor and the actual gas concentration is to use some specific nonlinear parametric models. In this work, we use the nonparametric model to depict the change in the gas sensor response with the gas concentrations, which effectively avoids model misspecification. Furthermore, we propose an inverse prediction method based on the nonparametric model to predict gas concentrations. Data-driven selection of tuning parameters is also developed. The simulation results reveal that, when the real model of the sensor array is unknown or misspecified, the nonlinear parametric model is inferior to the nonparametric model in performance. Meanwhile, we verify this with the real data analysis.