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基于增强的蝙蝠算法,智能启发的架构为弹性宏观经济预测提供了弹性

Sirong Mou¹, Junqi Gan², Yanze Yang³

¹The National University of Malaysia, UKM, Bangi, 43600, Selangor, Malaysia.

Scientific reports

|November 21, 2025

概括

本研究引入了一种增强的蝙蝠算法-反向传播神经网络 (EBA-BPNN) 模型,以提高宏观经济预测的准确性. 欧洲银行-BPNN模型大大减少了GDP预测中的错误,提供了更好的政策支持.

科学领域:

经济学经济学经济学
计算智能是一种计算智能.
数据科学数据科学数据科学

背景情况:

使用非线性,高维数据进行宏观经济预测是一个NP难题.
传统模型与局部最佳情况扎,限制了预测的准确性和可靠性.
准确的预测对于政策制定和全球风险管理至关重要.

研究的目的:

开发一种混合模型,EBA-BPNN,用于优化宏观经济预测中的反向传播神经网络 (BPNN).
解决传统模型在处理复杂经济数据方面的局限性.
提高预测准确性和可靠性,以获得更好的经济政策支持.

主要方法:

利用动态时间扭曲 (DTW) 来实现跨国经济周期对齐.
雇员格兰杰因果关系和核心变量选择 (32个变量) 的相互信息.
使用增强的蝙蝠算法 (EBA) 优化的BPNN,具有动态惯性权重,考希-高斯扰动和梯度辅助搜索.

主要成果:

与BPNN相比,EBA-BPNN在季度GDP预测中的平均绝对误差 (MAE) 降低了29.3%.
与PSO-BPNN相比,MAE降低了17.8%,与BA-BPNN相比降低了15.6%.
即使在极端经济情景下,也保持了有限的MAE增长 (12.3%).

结论:

EBA-BPNN模型为动态经济建模提供了一种高精度的方法.
与现有的宏观经济预测方法相比,提供了显著的改进.
支持基于证据的财政政策制定和跨境投资决策.

关键词:

反向传播神经网络的神经网络蝙蝠算法蝙蝠算法动态惯性重量是一个动态惯性重量.经济预测经济预测

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