Hybrid intelligence based modeling for nonlinear distributed parameter process with applications to the curing process

A spectral approximation based intelligent modelling method is proposed for the snap curing process, which belongs to nonlinear parabolic distributed parameter systems (DPSs). Unlike generic modelling approaches for DPSs, the proposed modelling method combines model reduction techniques of the snap curing process and intelligence based identification methods of nonlinear ODE (ordinary differential equation) systems. The exact model equations of the snap curing process do not need and only finite measurements are used in the modelling process. The built neural network model is of state space form that fits the general model-based controller formulations, thus the control techniques used for ODE models can be applied in the reduced-order model that represents the distributed parameter system. Moreover, the modelling process can be implemented offline or online. Experimental results show that the proposed modelling method is feasible and effective for a class of nonlinear DPSs.