1. A method for developing a process model for regulating a combustion process in a plant in which, while at least oxygen is supplied, material is converted by way of the combustion process with at least one flame being formed, and a state of a system in the plant is described by state variables, the method comprising: setting up a neuronal network; training the neuronal network using at least some measurement data of the state variables, wherein the measurement data comprises measurement data from a plurality of input channels and measurement data from at least one output channel; performing a first evaluation of the neuronal network, wherein the performing of the first evaluation comprises (a) calculating first values for the output channel using the neuronal network and at least some of the measurement data from the plurality of input channels, and (b) calculating a first standard deviation from deviations of the first values from at least some of the measurement data of the output channel; providing a distribution for use in place of measurement data from at least one input channel of the plurality of input channels; performing a second evaluation of the neuronal network, wherein the performing of the second evaluation comprises (a) calculating second values for the output channel using the neuronal network and predetermined data, wherein the predetermined data comprises at least a portion of the distribution, and (b) calculating a second standard deviation from deviations of the second values from at least some of the measurement data of the output channel; determining whether the at least one input channel is significant for the neuronal network, wherein the at least one input channel is significant if the second standard deviation is larger than the first standard deviation.