Design Hybrid Methods for Encoding Prior Knowledge in Feedforward Network with Application in Chemical Engineering

Three-layer feedforward networks have been widely used in modeling chemical engineering processes and prior-knowledge-based methods have been introduced to improve their performances. In this paper, we propose the methodology of designing better prior-knowledge-based hybrid methods by combining the existing ones. Then according to this methodology, two hybrid methods, interpolation-optimization (IO) method and interpolation-penalty-function (IPF) method, are designed as examples. Finally, both methods are applied to modeling two cases in chemical engineering to investigate their effectiveness. Simulation results show that the performances of the hybrid methods are better than those of their parents.