2025年 01期

摘要(Abstract)：

为了模拟回声状态网络模型在时间序列预测实例中的影响因素，在回声状态网络模型的储备池层引入高斯噪声，构建结合高斯噪声的回声状态网络模型；利用公式推导分析所提模型的非线性性质；采用股票序列数据与Logistic混沌序列数据进行实验验证和对比分析。结果表明，本文所提模型的预测效果优于回声状态网络模型、压缩感知回声状态网络模型和反向传播神经网络模型，股票收盘价预测、Logistic混沌序列预测的平均绝对误差均最小，分别为1.33×10~(-3)、 5.21×10~(-4)。

关键词(KeyWords)： 时间序列预测;回声状态网络模型;高斯噪声;储备池层;

基金项目(Foundation): 国家自然科学基金项目(62103165);; 算力互联网与信息安全教育部重点实验室开放课题项目(2023ZD038);; 山东省自然科学基金项目(ZR2022ZD01,ZR2023MF036);; 山东省科技型中小企业创新工程项目(2023TSGC0150)

作者(Author): 王梓鉴,赵慧,郑明文,李鑫

DOI: 10.13349/j.cnki.jdxbn.20241212.001

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