使用联合多创新分数梯度下降算法的分数顺序系统识别
Zishuo Wang1, Beichen Chen1,2, Hongliang Sun1
1School of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin, 132022, China.
Scientific reports
|December 27, 2024
概括
本研究介绍了一种新的联合多创新分数梯度下降算法,用于识别分数顺序系统. 该方法通过同时估计系统参数和分数顺序来提高准确性,并通过模拟和现实世界的实验来验证.
科学领域:
- 控制系统工程 控制系统工程
- 应用数学 应用数学 应用数学
- 信号处理 信号处理
背景情况:
- 分数顺序系统与整数顺序系统相比,提供了增强的建模功能.
- 准确识别参数和订单对于有效控制和分析这些系统至关重要.
- 现有的识别算法在有效地同时估计参数和订单方面可能面临挑战.
研究的目的:
- 开发一种新的联合多创新分数梯度下降算法,用于分数顺序系统识别.
- 提高在分数顺序系统中的参数和顺序估计的准确性和稳定性.
- 从理论上分析拟议算法的收性质.
主要方法:
- 利用微积分计算来设计一个联合的微积分梯度下降算法,同时进行参数和顺序估计.
- 使用估计参数作为订单识别的初始条件,反之亦然,用于代改进.
- 结合多创新理论来提高梯度下降方法的融合速度和识别精度.
主要成果:
- 拟议的联合多创新分数梯度下降算法有效估计分数顺序系统的参数和顺序.
- 与标准方法相比,该算法证明了系统识别准确度的提高.
- 理论趋同分析证实了拟议的识别技术的稳定性和可靠性.
结论:
- 开发的算法为识别分数顺序系统提供了有效的解决方案.
- 数字模拟和灵活链接系统上的实验验证证证了算法的实际适用性和有效性.
- 联合代方法和多创新策略显著提高了识别性能.
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