Study of Dynamic Systems Based on Lotka-Volterra Models and Machine Learning Methods

This study presents an optimized dynamic system analysis framework leveraging Lotka-Volterra models and machine learning techniques. The integration of genetic algorithms for parameter optimization enhances the model's predictive accuracy. The sensitivity analysis and the comprehensive evaluation model based on the TOPSIS method reveal that the model is highly sensitive to key parameters, indicating the importance of precise parameter tuning for reliable system predictions. The study concludes that the application of these methods provides a robust approach to dynamic system modeling and parameter optimization.