输出非线性Box-Jenkins-Wiener模型与ARMA噪声的等级代识别
Mahdi Nadi1, Mohammad Mehdi Arefi1
1Department of Power and Control Engineering, School of Electrical and Computer Engineering, Shiraz University, Shiraz, the Islamic Republic of Iran.
这项研究引入了一个新的三阶段代算法,用于维纳模型参数估计. 拟议的3S-GELSI算法提高了融合速度,并减少了用于系统识别的计算负载.
科学领域:
- 控制系统工程 控制系统工程
- 信号处理 信号处理
- 系统识别系统识别系统
背景情况:
- 准确的参数估计对于维纳模型至关重要,特别是对于复杂的噪声结构.
- 现有的代算法可能会受到缓慢的融合或高计算成本的影响.
- 自动回归移动平均线 (ARMA) 噪声在确定维纳系统的非线性和线性组件方面提出了挑战.
研究的目的:
- 开发一个高效的三阶段代算法,用于维纳模型的参数估计.
- 与现有方法相比,提高融合率并降低计算负担.
- 通过模拟验证拟议的算法的有效性.
主要方法:
- 基于通用扩展梯度代 (GEGI) 算法的三阶段代算法的开发.
- 导出第二个算法,即三步通用扩展最小平方代 (3S-GELSI) 算法,以提高性能.
- 模拟研究涉及三个不同的例子来评估系统识别准确性.
主要成果:
- 三阶段GEGI (3S-GEGI) 算法与单阶段对应算法相比,显示出更高的收率.
- 拟议的3S-GELSI算法显示了与现有方法相比,对汇率的显著改善.
- 3S-GELSI算法导致计算负担减少,由更少的浮点运算 (失败) 证明.
结论:
- 开发的三阶段代算法有效地识别了具有ARMA噪声的Wiener模型.
- 3S-GELSI算法为参数估计,平衡速度和计算成本提供了高效的方法.
- 这些发现为先进的系统识别技术提供了宝贵的贡献.
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