The method of neural network parameter function modeling has been demonstrated to be very effective in modeling the complex dynamics of cell growth and protein production in biotechnology processes. The development of neural network models usually requires large amounts of experimental data. This work examines the ability to use interpolated parameter functions in conjunction with neural network parameter function models to reduce the number of experiments required to develop neural network-based models of dynamic systems. Simulation and experimental results confirming the efficiency of the proposed method are presented.