Quantum-Inspired Neural Network Modeling of Uncertain Nonlinear Processes

Uncertain nonlinear processes are commonly encountered in chemical, energy, manufacturing, and environmental systems, where nonlinear dynamics, parameter variations, and external disturbances significantly complicate accurate modeling and prediction. Although conventional neural networks possess strong approximation capabilities, their performance often degrades under highly uncertain and time-varying operating conditions. Therefore, the development of more adaptive and robust intelligent modeling approaches remains a critical challenge in modern process engineering.

This study proposes a quantum-inspired neural network (QINN) framework for the identification and prediction of uncertain nonlinear processes. The proposed approach combines quantum-inspired computational principles with neural-network-based learning to enhance nonlinear mapping capability and uncertainty representation. The model was trained and validated using benchmark nonlinear process datasets containing parameter uncertainties and external disturbances. Prediction performance was evaluated using the root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R²).

Simulation studies demonstrated that the proposed QINN model effectively captured complex nonlinear process dynamics and provided accurate predictions under varying operating conditions. Compared with a conventional feedforward neural network, the proposed framework achieved lower prediction errors, faster convergence during training, and improved generalization performance. Furthermore, the model maintained stable predictive accuracy in the presence of process uncertainties and disturbance variations, indicating enhanced robustness and adaptability.

The obtained results confirm the potential of quantum-inspired learning mechanisms for improving the modeling and prediction of uncertain nonlinear systems. The proposed framework offers a promising solution for intelligent process monitoring, soft sensing, optimization, and advanced control applications. Future research will focus on real-time implementation, experimental validation, and integration with adaptive process control strategies in industrial environments.