Efficient orthogonalization in gas sensor arrays using reciprocal kernel support vector regression

In this paper support vector regression is presented, and it is used to model the responses of metal oxide gas sensors to combustion byproducts. A new version of the reciprocal kernel is presented for use in the regression, and it is tested in multiple dimensions. The orthogonality of the sensors is also calculated to determine if the sensors are suitable for use in arrays. A fast numerical approximation of the sensor orthogonality, which takes advantage of the reciprocal kernel, is presented as a way of quickly optimizing the effective response of large arrays. Comparison reveals advantages over standard approaches like principal component analysis.