In this paper, we address the problem of developing accurate neural network
equipment models economically. To this end, we propose model modifier tech
niques in conjunction with physical-neural network models. Two model modifi
ers-difference method and source input method-are proposed and evaluated on
a horizontal chemical vapor deposition reactor. The results show that the
source input method outperforms the difference method. Further, to develop
a model of comparable accuracy, the source input method reduces the number
of experimental data points to approximately one fourth of those needed wit
hout this approach.