Patent ID: 9702759
Date: 2017-07-11
CPC Classifications: G01J,G06N

Claim:
1. A method for applying a cost function to calculate an optimal value of a single variable for a calibration application, comprising: identifying, by a processor, the single variable of the calibration application; applying, by the processor, the cost function to the single variable, wherein the cost function comprises a function of a fit error plus a regularization weighting parameter (λ) times a regularization cost, wherein the cost function comprises a machine learning cost function of a delta (J(δ)), wherein the function ∥ ∥ F is a Frobenius norm of a matrix, ISIS_training_spectra are spectral values of a training data set of a reference device, M δ is a calibration matrix whose calculation is generated by a calibration process that is dependent on a parameter δ, ILS_training_spectra are spectral values of a training data set of a multi-function device being calibrated, E norm is a norm of an uncorrected device error represented as ∥ISIS_training_spectra−ILS_training_spectra∥ F , g is an optional normalization term equal to a nominal gain between the training data set of the reference device and the training data set of the multi-function device to be calibrated and I is the identity matrix, wherein the fit error is represented by ∥ISIS_training_spectra−M δ *ILS_training_spectra∥ F /E norm and the regularization cost is represented by ∥M δ −g*I∥ F ; calculating, by the processor, the optimal value of the single variable based upon the cost function that is applied to the single variable; and calibrating, by the processor, the multi-function device with the calibration application that uses the optimal value of the single variable to generate a calibration matrix used for the calibration application.