通过人工智能技术与控制方法预测并稳定两个宏观经济模型中的混沌状态
Forecasting and stabilizing chaotic regimes in two macroeconomic models via artificial intelligence technologies and control methods
AI总结：
针对经济模型混沌特性降低预测能力的问题，提出结合进化算法、连续深度Q学习与Pyragas控制法的方案，可稳定周期轨迹、抑制混沌，提升经济预测与机制调优的可靠性。
摘要：
经济学的核心任务之一是利用数学模型预测经济主体对经济变量未来值的预期。数学模型的行为可能呈现不规则性，包括混沌特性，这会降低其预测能力。本文研究了两个经济模型的行为状态，并识别出其中的不规则动力学。以这些模型为例，我们验证了进化算法、连续深度Q学习方法与Pyragas控制法结合用于推导控制作用的有效性，该控制作用可稳定不稳定周期轨迹并抑制混沌动力学。我们对比了施加控制前后模型动力学的定性与定量特征，并通过数值模拟验证了所得结果。所提出的方法可提升预测的可靠性，以及经济机制的调优效果，以实现决策效率最大化。
英文摘要：
One of the key tasks in the economy is forecasting the economic agents' expectations of the future values of economic variables using mathematical models. The behavior of mathematical models can be irregular, including chaotic, which reduces their predictive power. In this paper, we study the regimes of behavior of two economic models and identify irregular dynamics in them. Using these models as an example, we demonstrate the effectiveness of evolutionary algorithms and the continuous deep Q-learning method in combination with Pyragas control method for deriving a control action that stabilizes unstable periodic trajectories and suppresses chaotic dynamics. We compare qualitative and quantitative characteristics of the model's dynamics before and after applying control and verify the obtained results by numerical simulation. Proposed approach can improve the reliability of forecasting and tuning of the economic mechanism to achieve maximum decision-making efficiency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
