基于自适应的非线性几何模型的预测

Abstract

This paper introduces a novel prediction framework based on adaptive non-linear geometry models. Traditional predictive models often rely on fixed parameters, limiting their accuracy and efficiency. Our approach dynamically adjusts model parameters to capture the complex, non-linear relationships within the system being modeled. This adaptive mechanism significantly enhances prediction precision and speed compared to static models. We present a methodology for parameter adjustment, leveraging a reinforcement learning algorithm to optimize for model performance across a range of input data. This framework demonstrates improved accuracy and efficiency in predicting time-series data, particularly in scenarios involving complex dynamics and non-linear dependencies. We provide a comprehensive analysis of the algorithm's effectiveness through simulations and experimental validation. The core claim is that this adaptive framework achieves superior predictive performance through dynamic parameter adjustment.