Modeling and Simulation of a Class of Unified Nonlinear Characteristics in Chemical Processes

With the rapid advancement of artificial intelligence, neural networks have become a key tool in industrial system identification. Static nonlinearities, such as dead zone, saturation, loading, and hysteresis, are common in actuators, controlled objects, and sensors, with hysteresis being particularly prominent in sensors. This paper explores the modeling and prediction of static nonlinear characteristics using neural networks, focusing on a typical NL model that incorporates dead zone, saturation, and hysteresis. It evaluates the performance of BP and RBF neural networks in static nonlinear prediction and optimizes the selection of activation functions to improve generalization and accuracy. The findings offer theoretical and methodological insights for modeling complex nonlinear behaviors in industrial systems.