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    科学领域:

    • 机器学习 机器学习
    • 非线性动力学是一种非线性动力学.
    • 系统识别系统识别系统

    背景情况:

    • 经常性随机配置网络 (RSCNs) 显示出对复杂动态系统建模的前景.
    • 现有的方法可能缺乏对不确定的系统进行强大的概括和学习能力.

    研究的目的:

    • 提高RSCN的学习能力和通用化表现.
    • 为改进非线性系统建模开发混合规范化技术.

    主要方法:

    • 使用最小绝对缩小和选择运算符 (LASSO) 来识别时间数据中的重要变量.
    • 引入了改进的RSCN与L2调节,以模拟来自LASSO近似的残余.
    • 使用实时投影算法进行输出权重更新.

    主要成果:

    • 拟议的混合规范化方法显著改善了RSCN的性能.
    • 与现有模型相比,在非线性系统识别方面表现出更高的准确性.
    • 在所有数据集的两个工业预测任务中实现了高性能.

    结论:

    • 混合规范化方法有效地提高了非线性动态系统的RSCN能力.
    • 该方法为不确定性下的系统识别和预测建模提供了强大的解决方案.
    • 理论分析支持网络对复杂函数的普遍近似属性.