Prediction of CODLAG Propulsion System Parameters Using Kolmogorov-Arnold Network

Predicting the parameters of a Combined Diesel-Electric and Gas (CODLAG) propulsion system is crucial for optimizing the design, performance, and reliability of these complex engineering systems. This paper presents the implementation of the Kolmogorov-Arnold Network (KAN) for predicting CODLAG system parameters. The KAN, based on Kolmogorov's superposition theorem, can accurately approximate nonlinear relationships within the system. We provide a comprehensive overview of CODLAG systems, their operational principles, and the challenges in parameter prediction. Experimental results demonstrate that increasing hidden layers, optimizing learning rates, and adjusting batch sizes and epochs significantly improve prediction accuracy. The findings highlight the robustness of KAN in modeling complex systems and pave the way for future research to enhance the predictive capabilities of engineering systems.