A Novel Model Prediction and Migration Method for Multi-Mode Nonlinear Time-Delay Processes

Abstract

Most industrial processes have nonlinear and time-delay characteristics leading to difficulty in prediction modeling. In addition, the working conditions of most industrial processes are complex, which results in multiple modes. Testing and modeling for each mode is a waste of time and resources. Therefore, it is urgent to complete model migration between different modes. In this work, a new prediction model, a nonlinear autoregressive model with exogenous inputs and back propagation neural network (NARX-BP), is proposed for the nonlinear and time-delay processes, where the input data order of the model is determined by the feedforward neural network (FNN) method, and the nonlinear relation is realized by the BP neural network. For the multi-mode characteristic, a new migration optimization algorithm, input–output slope/bias correction and differential evolution (IOSBC-DE), is provided for using a small amount of data under a new mode to correct the slope and bias of the relationship between the input and output variables through DE. The modeling and migration methods are applied to a wind tunnel system, and the simulation result shows the effectiveness of the proposed method.