在未确定条件下,平行汉默斯坦模型的性能分析和系数生成方法
Nanzhou Hu1, Youyang Xiang1, Mingyang Li1
1Institute of Electronic Engineering, China Academy of Engineering Physics, Mianyang 621999, China.
Sensors (Basel, Switzerland)
|January 10, 2026
概括
本研究分析了对非线性系统的平行汉默斯坦 (PH) 模型. 使用奇数值分解 (SVD) 和最小平方 (LS) 的新方法简化了系数估计并提高了性能.
科学领域:
- 电气工程 电气工程
- 信号处理 信号处理
- 非线性系统建模 非线性系统建模
背景情况:
- 非线性信号模型对于功率放大器预扭曲和自我干扰取消至关重要.
- 平行汉默斯坦 (PH) 模型虽然有效,但由于其混合架构,在性能分析和系数估计方面存在挑战.
- 了解和优化PH模型性能对于先进的无线通信系统至关重要.
研究的目的:
- 分析平行汉默斯坦 (PH) 模型在具有记忆效应的非线性系统中的性能.
- 为PH模型开发一种高效的系数估计方法.
- 将PH模型的性能与内存多项式 (MP) 模型进行比较.
主要方法:
- 使用相同的基础函数对PH和内存多项式 (MP) 模型进行比较分析.
- 跨不同并行分支,非线性顺序和内存深度的性能评估.
- 在未确定条件下使用单数值分解 (SVD) 来推导PH模型的闭式性能表达式.
- 开发一种结合SVD和最小平方 (LS) 的系数生成方法.
主要成果:
- 获得了PH模型性能的闭式表达式,将其与MP模型系数矩阵的奇数值联系起来.
- 拟议的SVD-LS方法允许直接计算系数和实时性能评估.
- 模拟表明,选择与较大的单一值相对应的并行分支可以在减少复杂性的情况下获得接近最佳的性能.
结论:
- 该SVD-LS方法有效地解决PH模型系数估计和性能分析的挑战.
- 基于单数值的平行分支选择优化是实现高性能和高效率的关键.
- 这项研究为设计和实施先进的非线性信号处理技术提供了有价值的框架.
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